diff --git a/Jonas_Solutions/Task03_Instructions.py b/Jonas_Solutions/Task03_Instructions.py
new file mode 100644
index 0000000..17e8fe3
--- /dev/null
+++ b/Jonas_Solutions/Task03_Instructions.py
@@ -0,0 +1,84 @@
+import numpy as np 
+import pandas as pd 
+from sklearn.feature_extraction.text import CountVectorizer
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+from keras.models import Sequential
+from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
+from sklearn.model_selection import train_test_split
+from keras.utils.np_utils import to_categorical
+import re
+
+'''
+Task 3: playing with NN framwork/keras and basic sentiment analysis
+- use the following model as a baseline and improve it!
+- export your metadata (just basic hyperparameters and outcomes for test data!)
+- test data = 0.3 (not in this example, change it!)
+- random_state = 4222
+- no need to cross-validation!
+'''
+
+# parameters
+max_fatures = 500
+embed_dim = 128
+lstm_out = 196
+dropout = 0.1
+dropout_1d = 0.4
+recurrent_dropout = 0.1
+random_state = 1324
+validation_size = 1000
+batch_size = 16
+epochs=2
+verbose= 2
+
+df = pd.read_csv('dataset_sentiment.csv')
+df = df[['text','sentiment']]
+print(df[0:10])
+
+df = df[df.sentiment != "Neutral"]
+df['text'] = df['text'].apply(lambda x: x.lower())
+df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))
+df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\s]','',x)))
+    
+tok = Tokenizer(num_words=max_fatures, split=' ')
+tok.fit_on_texts(df['text'].values)
+X = tok.texts_to_sequences(df['text'].values)
+X = pad_sequences(X)
+
+nn = Sequential()
+nn.add(Embedding(max_fatures, embed_dim, input_length = X.shape[1]))
+nn.add(SpatialDropout1D(dropout_1d))
+nn.add(LSTM(lstm_out, dropout=dropout, recurrent_dropout=recurrent_dropout))
+nn.add(Dense(2, activation='softmax'))
+nn.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics = ['accuracy'])
+print(nn.summary())
+
+Y = pd.get_dummies(df['sentiment']).values
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.30, random_state = random_state)
+nn.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, verbose=verbose)
+
+X_validate = X_test[-validation_size:]
+Y_validate = Y_test[-validation_size:]
+X_test = X_test[:-validation_size]
+Y_test = Y_test[:-validation_size]
+score, accuracy = nn.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)
+print("score: %.2f" % (score))
+print("acc: %.2f" % (accuracy))
+
+pos_cnt, neg_cnt, pos_ok, neg_ok = 0, 0, 0, 0
+for x in range(len(X_validate)):
+    result = nn.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]
+    if np.argmax(result) == np.argmax(Y_validate[x]):
+        if np.argmax(Y_validate[x]) == 0: neg_ok += 1
+        else: pos_ok += 1
+    if np.argmax(Y_validate[x]) == 0: neg_cnt += 1
+    else: pos_cnt += 1
+
+print("pos_acc", pos_ok/pos_cnt*100, "%")
+print("neg_acc", neg_ok/neg_cnt*100, "%")
+
+X2 = ['what are u going to say about that? the truth, wassock?!']
+X2 = tok.texts_to_sequences(X2)
+X2 = pad_sequences(X2, maxlen=26, dtype='int32', value=0)
+print(X2)
+print(nn.predict(X2, batch_size=1, verbose = 2)[0])
diff --git a/Jonas_Solutions/Task_02_JonasWeinz.ipynb b/Jonas_Solutions/Task_02_JonasWeinz.ipynb
index e450952..52e963d 100644
--- a/Jonas_Solutions/Task_02_JonasWeinz.ipynb
+++ b/Jonas_Solutions/Task_02_JonasWeinz.ipynb
@@ -13,7 +13,9 @@
     "* Tutorial on Datacamp: https://www.datacamp.com/community/tutorials/scikit-learn-fake-news\n",
     "\n",
     "* liar dataset paper: https://www.cs.ucsb.edu/~william/papers/acl2017.pdf\n",
-    "    * dataset: https://www.cs.ucsb.edu/~william/data/liar_dataset.zip"
+    "    * dataset: https://www.cs.ucsb.edu/~william/data/liar_dataset.zip\n",
+    "\n",
+    "* Mex Vocabulary: http://jens-lehmann.org/files/2015/semantics_mex.pdf"
    ]
   },
   {
@@ -62,7 +64,7 @@
     "from IPython.display import display, Markdown, Latex\n",
     "import collections\n",
     "import traceback\n",
-    "import os"
+    "import os\n"
    ]
   },
   {
@@ -104,6 +106,9 @@
     "        print('Confusion matrix, without normalization')\n",
     "\n",
     "    thresh = cm.max() / 2.\n",
+    "    \n",
+    "    pp(cm)\n",
+    "    \n",
     "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
     "        plt.text(j, i, cm[i, j],\n",
     "                 horizontalalignment=\"center\",\n",
@@ -125,7 +130,53 @@
     "    score = metrics.accuracy_score(yt, pred)\n",
     "    pp(\"score: \" + str(score))\n",
     "    cm = metrics.confusion_matrix(yt, pred, labels=labels)\n",
-    "    plot_confusion_matrix(cm, classes=labels, title=title)"
+    "    plot_confusion_matrix(cm, classes=labels, title=title)\n",
+    "    return cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nfrom rdflib import Graph, Literal, BNode, RDF, Namespace\\nfrom rdflib.namespace import FOAF, DC, XSD\\n\\nmexcore = Namespace('http://mex.aksw.org/mex-core#')\\nmexperf = Namespace('http://mex.aksw.org/mex-perf#')\\nmexalgo = Namespace('http://mex.aksw.org/mex-algo#')\\nprov = Namespace('http://www.w3.org/ns/prov#')\\n\\ndef create_mex_graph():\\n    graph = Graph()\\n    graph.bind(mexcore)\\n    graph.bind(mexperf)\\n    graph.bind(mexalgo)\\n    graph.bind(prov)\\n    graph.bind(FOAF)\\n    graph.bind(DC)\\n    graph.bind(XSD)\\n    \\n    return graph\\n\\ndef mex_performance(experiment, model, dataset, performance, phase='Train', graph=create_mex_graph()):\\n    \\n    p = BNode()\\n    \\n\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "from rdflib import Graph, Literal, BNode, RDF, Namespace\n",
+    "from rdflib.namespace import FOAF, DC, XSD\n",
+    "\n",
+    "mexcore = Namespace('http://mex.aksw.org/mex-core#')\n",
+    "mexperf = Namespace('http://mex.aksw.org/mex-perf#')\n",
+    "mexalgo = Namespace('http://mex.aksw.org/mex-algo#')\n",
+    "prov = Namespace('http://www.w3.org/ns/prov#')\n",
+    "\n",
+    "def create_mex_graph():\n",
+    "    graph = Graph()\n",
+    "    graph.bind(mexcore)\n",
+    "    graph.bind(mexperf)\n",
+    "    graph.bind(mexalgo)\n",
+    "    graph.bind(prov)\n",
+    "    graph.bind(FOAF)\n",
+    "    graph.bind(DC)\n",
+    "    graph.bind(XSD)\n",
+    "    \n",
+    "    return graph\n",
+    "\n",
+    "def mex_performance(experiment, model, dataset, performance, phase='Train', graph=create_mex_graph()):\n",
+    "    \n",
+    "    p = BNode()\n",
+    "    \n",
+    "'''"
    ]
   },
   {
@@ -139,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -389,179 +440,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "X1, Xt1, y1, yt1 = train_test_split(df_1.drop('label', axis=1)['text'], df_1.label, test_size=0.25, random_state=4222)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vectorizer_1 = CountVectorizer(stop_words='english')\n",
-    "vec_train_1 = vectorizer_1.fit_transform(X1)\n",
-    "vec_test_1 = vectorizer_1.transform(Xt1)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#tfidf_vectorizer_1 = TfidfVectorizer(stop_words='english', max_df=0.7)\n",
-    "#tfidf_train_1 = tfidf_vectorizer_1.fit_transform(X1)\n",
-    "#tfidf_test_1 = tfidf_vectorizer_1.transform(Xt1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#display(count_vectorizer.get_feature_names()[0:10])\n",
-    "#display(count_vectorizer.get_feature_names()[-10:])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#display(tfidf_vectorizer.get_feature_names()[:10])\n",
-    "#display(tfidf_vectorizer.get_feature_names()[-10:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#count_df = pd.DataFrame(count_train.A, columns=count_vectorizer.get_feature_names())\n",
-    "#tfidf_df = pd.DataFrame(count_train.A, columns=tfidf_vectorizer.get_feature_names())\n",
-    "#diff = set(count_df.columns) - set(tfidf_df.columns)\n",
-    "#pp(count_df.equals(tfidf_df))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#clf = MultinomialNB()\n",
-    "#clf.fit(tfidf_train_1, y1)\n",
-    "#pred = clf.predict(tfidf_test_1)\n",
-    "#score = metrics.accuracy_score(yt1, pred)\n",
-    "#pp(\"score: \" + str(score))\n",
-    "#cm = metrics.confusion_matrix(yt1, pred, labels=[\"FAKE\", \"REAL\"])\n",
-    "#plot_confusion_matrix(cm, classes=[\"FAKE\", \"REAL\"], title= \"TFIDF_Vecctorizer, Multinomial Naive Bayes\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "'score: 0.9320143127762577'\n",
-      "Confusion matrix, without normalization\n",
-      "'score: 0.8838383838383839'\n",
-      "Confusion matrix, without normalization\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f458289be80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f45828bbd30>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clf_a = MultinomialNB()\n",
-    "clf_a.fit(vec_train_1, y1)\n",
-    "test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- train\", Xt=vec_train_1,yt=y1, clf=clf_a)\n",
-    "test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- test\", Xt=vec_test_1,yt=yt1, clf=clf_a)"
+    "vectorizer_1 = TfidfVectorizer(stop_words='english', max_df=0.7)\n",
+    "vec_train_1 = vectorizer_1.fit_transform(X1)\n",
+    "vec_test_1 = vectorizer_1.transform(Xt1)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* try to get most important features"
+    "* trying a Random Forest classifier "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'score: 0.9960008419280152'\n",
+      "Confusion matrix, without normalization\n",
+      "array([[2342,    3],\n",
+      "       [  16, 2390]])\n",
+      "'score: 0.8478535353535354'\n",
+      "Confusion matrix, without normalization\n",
+      "array([[737,  82],\n",
+      "       [159, 606]])\n"
+     ]
+    },
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "'\\ndef most_informative_feature_for_binary_classification(vectorizer, classifier, n=100):\\n    \"\"\"\\n    See: https://stackoverflow.com/a/26980472\\n    \\n    Identify most important features if given a vectorizer and binary classifier. Set n to the number\\n    of weighted features you would like to show. (Note: current implementation merely prints and does not \\n    return top classes.)\\n    \"\"\"\\n\\n    class_labels = classifier.classes_\\n    feature_names = vectorizer.get_feature_names()\\n    topn_class1 = sorted(zip(classifier.coef_[0], feature_names))[:n]\\n    topn_class2 = sorted(zip(classifier.coef_[0], feature_names))[-n:]\\n    \\n    l = []\\n    \\n    for coef, feat in topn_class1:\\n        l.append((class_labels[0], coef, feat))\\n\\n    display(l)\\n\\n    for coef, feat in reversed(topn_class2):\\n        l.append((class_labels[1], coef, feat))\\n    \\n    display(l)\\n\\n\\nmost_informative_feature_for_binary_classification(tfidf_vectorizer_1, linear_clf, n=30)\\n'"
+       "<matplotlib.figure.Figure at 0x7f5d5e84a8d0>"
       ]
      },
-     "execution_count": 16,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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VOh53Hcs/L8/XW6jzht11J8MfegBJfHXf/fj9Aw9x6TeH8M47E2nQoAHdu/fgnvt+R4MGDfIdamHJ7hzIEjPrXsWy+sBBwJVm9qaku9nc3QXAzEyS1TREbwHm2fQ5i+g58HZ6Drydw8/7OeVr1jH6pSncOWI8Pc75GT0H3s4/Xp3GD4b2BeDOh8dvWv+m34zm1UnTPfklaP78+dz722G8PmEikyZPY8OGDfzl8ccYeN75TJn2IRPffY/Va1bz0IMP5DvUwpKbY4DzgHlm9mZ8/ldCQlyY6trGn4vi8vlAx7TXd4jzquQJsIAc22NPPp63mP8uWM7KVWs2zW/apBFm//tHbkCf7ox6blJthliS1q9fz+rVq8PP8nLa7bwzffr229SN6969B/Pnz8t3mAVnW48BmtmnwFxJe8ZZvYF/A6OBwXHeYOCp+Hg0MCieDe4JrEjrKlfKu8AF5OyTDt4iod18+amcf0oPVnyxmj5Dh22xbpPGDTjh8L255vZRtR1mSWnfvj1XX/Nd9ti1E02aNKH38Sdy/Aknblq+bt06Rv7pEX555915jLJA5eYywCuBP0lqCMwCLiI03EZJGgLMAQbEdccA/YAZQHlct1qJtQAlbZA0OW3qkrbsLknzpc0Dh0q6UNI98XGZpBGS/hiz+WxJ76Vta9j/7rG4Nahfj5OP2Y+/jXt307ybf/s0Xfv+kMf+MZFLzzl6i/VPPno/3pg8y7u/CVu+fDnPPP0UH0z/mFn//YRV5asY+adHNy3/9hXf4oijjubII4/KY5SFaVtbgABmNtnMupvZ/mbW38yWm9lSM+ttZl3N7HgzWxbXNTO73Mx2M7P9zGxipu0n2QVebWbd0qbZEJIbcAbhbM0xFV+k8KncDzQALrbNfb9j07Z1VYJx58VJR+7D5A/nsmjZyv9Z9viYt+nfu9sW884+6WD+4t3fxL04/gW6dNmFNm3a0KBBA/r3P5MJb/wLgNt+cguLlyzmF7+6I89RFh5JuboOMFH5iKAX8D7hIsVzK1k+DGgFDDKzjbUYV15VPJ63W6c2mx6f0mt/Ppq9cNPzHZo15siDd+fpl6fWaoylqGPHTrz11gTKy8sxM156cTx77rU3Dz34AOOeH8vDj44siF/kQpSLFmDSkjwG2ETS5Pj4YzM7Iz4+FxhJOHD5U0kNzGxdXHYe4UrvXma2vsL2XpK0IT4eYWZ3VtxhvIYoXEfUoFnu3knCmjZuyHGH7sUVt47cNO/Wq06na+ed2LjR+O+CZVx122Oblp127AGMn/Ah5WvW5iPcktLj0EM548yzOKzHQdSvX58DDjiQId8cSqvm29Gpc2d6HXkYAKefcSbX33hTnqMtMPnPbxmpsrOLOdmw9IWZNaswryHwMbCXma2U9Dfgj2b2jKQLga8DewHnmNnraa+bDXQ3syXZ7r+s6U7WaM8BmVd0tWr52/fkOwRXwRGHdmfSpIk5TVeN2na19udnPjH08Z0nT6rmOsDE1fZZ4JOAFsB7sfnbFFgNPBOXfwjcRDjDc5KZvV/L8TnnckCCsiKoB1jbBy/OJZzY6GJmXQi3upwgqWlqBTP7F3AZ8IykTrUcn3MuJ7K+FS6vaq0FGJNcH+DS1DwzWyXpNeDU9HXN7GlJrYHnJKWuL0g/BjjVzAbVRtzOuZopgPyWUWIJsOLxPzMrB1pWst6ZaU+Hp81/CHgoPu2S+widc4kpki6w3wninMs54QnQOVfCSroL7JwrbYVwkiMTT4DOuZwrlstgPAE65xJQGJe5ZOIJ0DmXiCLIf54AnXPJ8Bagc64k+TFA51xJK4IGoCdA51wyvAvsnCtZRZD/PAE653LPjwE650qYXwfonCthRZD/fGB051wyclEQtcKQuBPjvJaSxkmaHn/uGOdL0jBJMyRNlXRQpu17AnTO5VzqGGCmKUupIXFTY4dcB4w3s67A+PgcoC/QNU5DCSNPVssToHMuEQmWxD8dGBEfjwD6p81/OA6QPgFoIalddRvyBOicS4SUeQJaS5qYNg2tsBkDnpc0KW1ZWzNbEB9/CrSNj9sDc9NeOy/Oq5KfBHHOJSLLFt6SDMNiHmlm8yXtBIyT9GH6QjMzSTUe29cToHMu56StOsZXJTObH38ukvQk0ANYKKmdmS2IXdxFcfX5QMe0l3eI86rkXWDnXCKy7AJX83ptJ2n71GPgRGAaMBoYHFcbDDwVH48GBsWzwT2BFWld5Up5C9A5l4iybb8QsC3wZOxK1wf+bGbPSXobGCVpCDAHGBDXHwP0A2YA5cBFmXZQZQKUtEN1LzSzz7N5B8650rSt+c/MZgEHVDJ/KdC7kvkGXL41+6iuBfg+4QxM+ttIPTeg09bsyDlXOiSoV8z3AptZx6qWOedcJsVwL3BWJ0EkDZR0fXzcQdLByYblnCt223oSpDZkTICS7gGOBS6Is8qB+5MMyjlX3ATUkzJO+ZbNWeDDzewgSe8CmNkySQ0Tjss5V8y27Va3WpNNAlwnqYxw4gNJrYCNiUblnCt6RZD/sjoG+FvgCaCNpFuA14CfJxqVc66oiXAdYKYp3zK2AM3sYUmTgOPjrLPNbFqyYTnnil1dKolfD1hH6Ab77XPOuWoVylneTLI5C3wDMBLYmXBz8Z8l/SDpwJxzxa1OdIGBQcCBZlYOIOk24F3gZ0kG5pwrbvlPb5llkwAXVFivfpznnHOVEkV+K5ykOwnH/JYB70saG5+fCLxdO+E554pSHbgOMHWm933g2bT5E5ILxzlXVxRB/qu2GMKDtRmIc65uKfYWIACSdgNuA/YBGqfmm9keCcblnCtixXIMMJtr+oYDDxHeU19gFPB4gjE55+oAZTHlWzYJsKmZjQUws5lmdiMhETrnXKWkunMd4JexGMJMSZcSRlnaPtmwnHPFrgDyW0bZJMBrgO2AqwjHApsD30gyKOdc8asT9wKb2Zvx4Uo2F0V1zrkqicLo4mZS3YXQTxJrAFbGzM5MJCLnXPErkmII1bUA76m1KBKw7x4dGT3uV/kOw1XQ5bK/5jsEV8HS/y5PZLu5ug5QUj1gIjDfzE6RtAvwGNAKmARcYGZrJTUCHgYOBpYC55jZ7Oq2Xd2F0ONzEr1zruSkxgTJkW8DHwCpscp/DtxpZo9Juh8YAtwXfy43s90lDYzrnVPdhr22n3MuEWXKPGUiqQNwMvBAfC7gOCDVlRgB9I+PT4/Pict7K0Mz1BOgcy4RWSbA1pImpk1DK2zmLuD7bB6HqBXwmZmtj8/nAe3j4/bAXIC4fEVcv0rZVoRGUiMz+zLb9Z1zpStUhM6qC7zEzLpXvg2dAiwys0mSeuUyvpRsKkL3kPQeMD0+P0DSb5IIxjlXd9QryzxlcARwmqTZhJMexwF3Ay0kpRpvHQg3ZxB/dgSIy5sTToZUKZsu8DDglNSGzGwKYaB055yrVC5GhTOzH5hZBzPrAgwEXjSz84GXgLPiaoOBp+Lj0fE5cfmLZlblpXyQXQIsM7M5FeZtyOJ1zrkSVpbFVEP/B1wraQbhGF+qdN+DQKs4/1rgukwbyuYY4FxJPQCL1+NcCXxUo7CdcyVBUk7LYZnZy8DL8fEsoEcl66wBzt6a7WaTAC8jdIM7AQuBF+I855yrUrHfCQKAmS0i9L+dcy5rRVALIauK0H+gknuCzazi9TrOOQdsPglS6LLpAr+Q9rgxcAbxYkPnnKuUsrrMJe+y6QJvUf5e0iPAa4lF5JyrE1QQRe+rl/WdIGl2AdrmOhDnXN0RusD5jiKzbI4BLmfzMcAywkDpGa+vcc6VtqJPgLGSwgFsvtVkY6Yrq51zrk4MixmT3Rgz2xAnT37OucyUKohQ/ZRv2ZynmSzpwMQjcc7VKUU9LKak+rGm1oHA25JmAqsIrVszs4NqKUbnXJGpCydB3gIOAk6rpVicc3WGclkSPzHVJUABmNnMWorFOVdHiMI4xpdJdQmwjaRrq1poZnckEI9zri7IcsyPfKsuAdYDmkERXM7tnCs4hXCSI5PqEuACM/txrUXinKsziuU6wIzHAJ1zriaKoAFYbQLsXWtROOfqFFEcY+5WmQDNbFltBuKcq0OyHxYzr2pSDcY556olKPrrAJ1zrsYKP/15AnTOJaQIGoBFcZzSOVd0hJR5qnYLUmNJb0maIul9SbfE+btIelPSDEmPS2oY5zeKz2fE5V0yRekJ0DmXc6ljgJmmDL4EjjOzA4BuQB9JPYGfA3ea2e7AcmBIXH8IsDzOvzOuVy1PgM65RCiLqToWfBGfNoiTAccBf43zRwD94+PT43Pi8t7K0Mz0BOicy714GUwWXeDWkiamTVsMtyupnqTJwCJgHDAT+CyW6gOYB7SPj9sTR6yMy1cAraoL00+COOdybisug1liZt2rWmhmG4BukloATwJ75SbCwFuAzrlEbGsXOJ2ZfQa8BBwGtJCUarx1YPOYRfOBjhAKOgPNgaXVbdcToHMuEds6JoikNrHlh6QmwAnAB4REeFZcbTDwVHw8Oj4nLn8x0zhG3gV2zuVcuBd4my8EbAeMkFSP0FgbZWbPSPo38JikW4F3gQfj+g8Cj0iaQRi+d2CmHXgCdM4lYNsHPTKzqYQxiSrOnwX0qGT+GuDsrdmHJ0DnXCKK4U4QT4DOuZzLURc4cZ4AnXO5VyADn2fiCdA5l4hiHxPE1YLvX3UJL477B61at2Hsq5MAuOsXt/LYI3+kZas2AHzvhls49oQ+rF27lhu+cwXvTXkHlZXxo9t+Rc8jjs5n+HXWDk0acMfgg9lz5x0w4JrhE5n56Up+d0lPOrZqytyl5Qz93QRWlK8D4PA92vDjgQfQoJ5YtnItZ/zqn/l9A3lWFwZGd7XgawMvYNCQS/nOFRdvMf8bl17J0Muv2WLeY4/8EYDnXpnIksWLuGhgf54a9xplZX45Z67dOvAAXpz2KRffP4EG9USThvX5dr+9ePWDRdzz3H+4os+eXNl3L2594j12aNKA288/kHPvfpX5y1bTevtG+Q6/IKgIjgH6b06eHXr4kbTYsWVW607/z4ccdlQvAFq32Ykdmjdn6uRJCUZXmrZvUp+ee7Thz6/NBmDdBuPz1es4qdvOjHpjDgCj3phDn247A3DmoR159t35zF+2GoAlK7/MS9yFZlsvhK4NngAL1MMP3k+fYw7h+1ddworPlgOw97778cJzz7B+/XrmzpnNe1PeZcH8eXmOtO7p1Ho7lq78krsv6s64H/bm14MOpmnDerTZoRGLVqwBYNGKNbTZIbT0dm27PS2aNuBv3z2GsTf25uzDOuUz/IKQo3JYiUssAUraIGmypGmSnk67paWLpNVxWWoalPa6bpJMUp8K2/ui4j7qqvMv/Cb/fPvfjHnpTdq0/Qq33XQdAAPOG0y7ndtz2vFH8OMbv8fBh/SkXr16eY627qlfVsZ+nVow/OVZnPCT8ZR/uZ4r+v7vPfipm6zql4n9O+/I14e9xrl3vco1J+/Nrm2b1XLUhUZZ/cu3JI8BrjazbgCSRgCXA7fFZTNTyypxLvBa/PlcgvEVrDY7td30+NwLvsGQ888EoH79+vzw1l9uWva1fr3YZbeutR5fXffJ8nIWLF/Nux+HgRGfeWc+V/bZk8Wff8lOzRuzaMUadmreeFNX95Plq1m+ai3lazdQvnYDE6Yv4asdmjNrYcn8zf5fBdLFzaS2usBvsLlmV5Vi8cKzgQuBEyQ1TjiugrTo0wWbHo8d8xR77LUPAKvLyylftQqAV18eT7169em65955ibEuW/z5l8xfvprdYivuqL124qMFn/P8lE8YcFhnAAYc1pmxkz8BYOzkT+ixe2vqlYkmDetx0C4tmb5gZd7iLxS5rAaTlMTPAscbmXuz+YZlgN1ikcOUK83sVeBw4GMzmynpZeBk4Imt2NdQYCjAzh06bmvoteKqoYOY8PqrLF+2hMP2342rv/9DJvzrFT6YNhUkOnTszE9/9RsAli5ZzKABp1JWVsZX2u3MHfc+mGHrrqZuGPku917cgwb1y5izeBVXD59ImcTvL+nJeUd2YV68DAZg+qcreWnap7z0oxPYaMafXv2YDz/5PM/vIL+KZVhMZagWU/MNSxuA9wgtvw+AY81sQxyo5Bkz27eS19wDTDGzP0g6DRgwcA0vAAAMlklEQVRkZmfFZV+YWdYHVvbvdrCNfuH1HLwTl0s9f/BMvkNwFSz9+/+xbvHMnGarvfc70B76+0sZ1zts9x0nVVcQNWlJdoFTxwA7E/4gXF7dyrGl+DXgJkmzgd8QBkHZPsEYnXMJKYaTIIkfAzSzcuAq4DtpVVwr0xuYamYdzayLmXUmdH/PSDpG51zu+XWAkZm9C0wlnNmFeAwwbboqLnuywkufSHtNU0nz0qZrayN251zNFEMCTOwkSMXjdWZ2atrTJlluYzShzDVm5hdtO1ckwlneAshwGfi9wM653CuQFl4mngCdc4kogvznCdA5l4RNA58XNE+AzrlEFEH+8wTonMu9QrnVLRM/s+qcS4SkjFOG13eU9JKkf0t6X9K34/yWksZJmh5/7hjnS9IwSTMkTZV0UKYYPQE65xKRg+sA1wPfMbN9gJ7A5ZL2Aa4DxptZV2B8fA7QF+gap6HAfZl24AnQOZeIba0GY2YLzOyd+HgloaZAe+B0YERcbQTQPz4+HXjYgglAC0ntqtuHJ0DnXO5lk/1CBmwtaWLaNLTSzYUiKgcCbwJtzSxVM+5TIFVAsz0wN+1l88hQhs9Pgjjnci6MCpfVaZAlmarBSGpGuC32ajP7PP3YoZmZpBqXtPIWoHMuEbkoiCqpASH5/cnM/hZnL0x1bePPRXH+fCC9EGiHOK9KngCdc8nYxgwYK8Q/CHxgZnekLRoNDI6PBwNPpc0fFM8G9wRWpHWVK+VdYOdcInJQDOEI4ALgvbQK8tcDtwOjJA0B5gAD4rIxQD9gBlAOXJRpB54AnXOJKNvG/Gdmr1F1O7F3JesbGQovV+QJ0DmXjCK4FcQToHMu57weoHOudHk9QOdcKfME6JwrUYUx6lsmngCdc4nwFqBzriQVSz1AT4DOuUR4SXznXMkqgvznCdA5l4wiyH+eAJ1zCfDrAJ1zpUr4MUDnXAkr/PTnCdA5l5AiaAB6AnTOJcPvBHHOlSxvATrnSlKW4/7mnSdA51wivAvsnCtZ3gJ0zpUsT4DOuRLl9QCdcyUq3AmS7ygy84HRnXOJSJ0Jrm7KvA39UdIiSdPS5rWUNE7S9PhzxzhfkoZJmiFpqqSDMm3fE6BzLhHK4l8WhgN9Ksy7DhhvZl2B8fE5QF+ga5yGAvdl2rgnQOdc7mXR+sumBWhmrwDLKsw+HRgRH48A+qfNf9iCCUALSe2q274nQOdczinLCWgtaWLaNDSLzbc1swXx8adA2/i4PTA3bb15cV6V/CSIcy4RWZbDWmJm3Wu6DzMzSVbT13sL0DmXiFx0gauwMNW1jT8XxfnzgY5p63WI86rkCdA5l4gsu8A1MRoYHB8PBp5Kmz8ong3uCaxI6ypXyrvAzrlk5OA6QEkjgV6EY4XzgB8BtwOjJA0B5gAD4upjgH7ADKAcuCjT9j0BOudyTkBZDq6ENrNzq1jUu5J1Dbh8a7av8Jq6R9Jiwl+HuqA1sCTfQbgt1KXvpLOZtcnlBiU9R/iMMlliZhWv86s1dTYB1iWSJm7LmTKXe/6d1A1+EsQ5V7I8ATrnSpYnwOLw+3wH4P6Hfyd1gB8DdM6VLG8BOudKlidA51zJ8gRYhCS1yncMztUFngCLjKQTgbsk7agsy2245Ph3UNw8ARaRmPx+CTxoZsvxWxkLQSsASf67VIT8SysSkvoQkt8lZvaypI7A9ZKyud3I5VisOLITMEfSaWa20ZNg8fEvrHgcCjQ1swmS2gBPAovMrK7cj1pUYtn1RYSKIw9J6pdKgpLq5Ts+lx3vQhU4SUcAx5jZLZJ2lfQG4Q/X78zsD2nrdTSzuVVuyCXCzEZJWgs8JulcM3s21RKUdGpYxZ7Jb5SuKt4CLFBp3akTgeYAZjYYeAXYsULyOx8YJmn7Wg+0xEjqI+kmSYen5pnZ3wktwccknRJbgpcA9wMf5itWl5m3AAtXc2A5sAbY1KUys/+T1EbSS2Z2rKSvAdcAg8xsZZ5iLSVHA5cBfeJYtb8FZpnZE/GM8HBJzwA9gH5mNiOPsboMvAVYgCTtAvxM0q7AQmD7OL8JgJl9A5glaQFwPSH5/Ttf8ZaYp4EXgDMJVYfPAR6RtKuZ/ZVQnfg04Dwzm5K/MF02vAVYmBoTBnq5BNiJzUP9NZK0Jh6AHyLpu8AYT37JkrQX8KWZfWxmb0hqBFxtZldLOo8wMHczSfOBu4CvmNnafMbssuPFEAqUpH2Bk4ArgU6EAV8OBD4B1gJfAP3NbF3egiwBkvoBPwQuSHVnJe0ODAX+Q2iBX0z4Xg4HXjazj/MUrttK3gIsEJJ6Eb6PV83sSzObJmkdsB2wNzAceA9oRugSL/bklyxJJxGS381mNkNSM8CApYQ/SpcDfc3slbj+R+YtiqLiLcACIKk58AywK3A3sMHMfh2X7QoMBNoBj5jZW3kLtIRI2g+YAhxvZi9K2g34HXCtmU2Ny0cAZ5vZzHzG6mrOT4IUADNbQUiAa4GPgL6Shks6A1hMONO4HBggqbHff5qctM92NuFi8wGSuhAKoI6Nya/MzN4jXJLUyy98Ll6eAPNI0lfSfuHuAP4BrDSz44GGcd4rwDHx50/NbI13sxLVECBeUnQ+4ZDDTODvZvbLmPw2SupG6Ao/Z2Yb8heu2xaeAPNE0smEExut0y56Xgh0i93ensCFhLOKZwLvmtmyfMRaKmKxicck3SzpTDNbQzgT/2fgMICY/IYAw4A/mNn8/EXstpUfA8yDWNjgBuA2M3tOUkMzWxsLHEwknOQYkLqFSlJTMyvPY8h1XvxObgEeJlx6tDPwCzObHu+wuZdwAuR54FLgUjOblq94XW54AqxlkloSBtQ+08z+Hg+u3wR8z8wWSfomcICZXZFKjHkNuASkfSenm9nTkjoAtwH3m9kbcZ2GwOOEWxMP8Wsv6wbvAtey2I09FbhJ0v6Eg+vvxsoiEM48HidpD09+tSPtO7ld0g5mNg9oDfxS0l2SriVcjjQE2N2TX93h1wHmQawYsgGYDFxvZndJqmdmG8zsLUkj8x1jqYnfyUZgkqTnCI2DXwNtCBc6fxW4xo/D1i3eBc4jSScAvwEONbMVkhqZ2Zf5jquUSTqecJyvnZktjPPKgJZee7Hu8S5wHpnZOEIll7cktfTkl39m9gJwMvBSrPiMmW305Fc3eRc4z8zsH/EA+wuSuhOLDec7rlKW9p08J6m7mW3Md0wuGd4FLhCSmpnZF/mOw23m30nd5wnQOVey/Bigc65keQJ0zpUsT4DOuZLlCdA5V7I8AdZhkjZImixpmqS/SGq6DdvqFUc7Q9Jpkq6rZt0Wkr5Vg33cHMc5yWp+hXWGSzprK/bVJY7q5kqYJ8C6bbWZdTOzfQnFVi9NX6hgq/8PmNloM7u9mlVaAFudAJ2rbZ4AS8erwO6x5fMfSQ8D04COkk6U9Iakd2JLsRlsGgT8Q0nvEGoSEudfKOme+LitpCclTYnT4cDtwG6x9fnLuN73JL0taaqkW9K2dYOkjyS9BuyZ6U1I+mbczhRJT1Ro1R4vaWLc3ilx/XqSfpm270u29YN0dYcnwBIgqT7QlzCoEkBX4F4z+yqwCriRMPbFQYR6hNdKagz8gVAl5WDgK1VsfhjwTzM7ADgIeJ8wTOTM2Pr8Xiw02pUwWHg34GBJR0s6mDDeSTegH3BIFm/nb2Z2SNzfB4QKLSld4j5OBu6P72EIsMLMDonb/6bCuMvO+a1wdVwTSZPj41eBBwmFPueY2YQ4vyewD/B6rM7fEHgD2Av42MymA0h6lDAUZEXHAYMAYmn4FZJ2rLDOiXF6Nz5vRkiI2wNPpoq9ShqdxXvaV9KthG52M2Bs2rJR8ba16ZJmxfdwIrB/2vHB5nHfH2WxL1fHeQKs21abWbf0GTHJrUqfBYwzs3MrrLfF67aRgJ+Z2e8q7OPqGmxrOGE85CmSLgR6pS2reFuTxX1faWbpiRKFgY5cifMusJsAHKEw2DeStpO0B/Ah0CVWrAY4t4rXjwcui6+tpzDE50pC6y5lLPCNtGOL7WOllVeA/pKaxLLzp2YR7/bAAkkNCIMWpTtbUlmMeVfCwOVjgcvi+kjaQ9J2WezHlQBvAZY4M1scW1IjJTWKs280s48kDQWelVRO6EJvX8kmvg38XmGgoA3AZWb2hqTX42Um/4jHAfcG3ogt0C+Ar5vZO5IeJ1TBXgS8nUXIPwTeJAwX+maFmP4LvAXsQBizY42kBwjHBt9R2PlioH92n46r67wYgnOuZHkX2DlXsjwBOudKlidA51zJ8gTonCtZngCdcyXLE6BzrmR5AnTOlaz/B/7S2JCTsTIXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5d5e84a048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "'''\n",
-    "def most_informative_feature_for_binary_classification(vectorizer, classifier, n=100):\n",
-    "    \"\"\"\n",
-    "    See: https://stackoverflow.com/a/26980472\n",
-    "    \n",
-    "    Identify most important features if given a vectorizer and binary classifier. Set n to the number\n",
-    "    of weighted features you would like to show. (Note: current implementation merely prints and does not \n",
-    "    return top classes.)\n",
-    "    \"\"\"\n",
-    "\n",
-    "    class_labels = classifier.classes_\n",
-    "    feature_names = vectorizer.get_feature_names()\n",
-    "    topn_class1 = sorted(zip(classifier.coef_[0], feature_names))[:n]\n",
-    "    topn_class2 = sorted(zip(classifier.coef_[0], feature_names))[-n:]\n",
-    "    \n",
-    "    l = []\n",
-    "    \n",
-    "    for coef, feat in topn_class1:\n",
-    "        l.append((class_labels[0], coef, feat))\n",
-    "\n",
-    "    display(l)\n",
-    "\n",
-    "    for coef, feat in reversed(topn_class2):\n",
-    "        l.append((class_labels[1], coef, feat))\n",
-    "    \n",
-    "    display(l)\n",
-    "\n",
-    "\n",
-    "most_informative_feature_for_binary_classification(tfidf_vectorizer_1, linear_clf, n=30)\n",
-    "'''\n"
+    "from sklearn.ensemble import RandomForestClassifier as RFC\n",
+    "clf_a = RFC(criterion='entropy', random_state=4222)\n",
+    "max_size=10000\n",
+    "clf_a.fit(vec_train_1[:max_size], y1[:max_size])\n",
+    "test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- train\", Xt=vec_train_1,yt=y1, clf=clf_a)\n",
+    "cm_1 = test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- test\", Xt=vec_test_1,yt=yt1, clf=clf_a)"
    ]
   },
   {
@@ -576,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1157,7 +1107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1171,58 +1121,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "vectorizer_2 = CountVectorizer(stop_words='english', max_df=0.7)\n",
+    "vectorizer_2 = TfidfVectorizer(stop_words='english', max_df=0.7)\n",
     "vec_train_2 = vectorizer_2.fit_transform(X2)\n",
     "vec_test_2 = vectorizer_2.transform(Xt2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "clf_b = MultinomialNB()\n",
-    "clf_b.fit(vec_train_2, y2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "'score: 0.8760555706891855'\n",
+      "Object `MLPClassifier` not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "?MLPClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* trying a MLP as classifier "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
+       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
+       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
+       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
+       "       nesterovs_momentum=True, power_t=0.5, random_state=4222,\n",
+       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
+       "       verbose=False, warm_start=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neural_network import MLPClassifier\n",
+    "clf_b = MLPClassifier(hidden_layer_sizes=(100,), random_state=4222)\n",
+    "clf_b.fit(vec_train_2, y2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'score: 0.9997275946608554'\n",
       "Confusion matrix, without normalization\n",
-      "'score: 0.6105032822757112'\n",
+      "array([[1675,    1],\n",
+      "       [   0, 1995]])\n",
+      "'score: 0.5776805251641138'\n",
       "Confusion matrix, without normalization\n",
-      "'score: 0.6527777777777778'\n",
-      "Confusion matrix, without normalization\n"
+      "array([[109,  99],\n",
+      "       [ 94, 155]])\n",
+      "'score: 0.6041666666666666'\n",
+      "Confusion matrix, without normalization\n",
+      "array([[ 92,  77],\n",
+      "       [ 94, 169]])\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f45827aaf98>"
+       "array([[ 92,  77],\n",
+       "       [ 94, 169]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5d5e737f28>"
       ]
      },
      "metadata": {},
@@ -1230,9 +1228,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f45828673c8>"
+       "<matplotlib.figure.Figure at 0x7f5d5b205fd0>"
       ]
      },
      "metadata": {},
@@ -1240,9 +1238,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f457ff9bfd0>"
+       "<matplotlib.figure.Figure at 0x7f5d5b1ebfd0>"
       ]
      },
      "metadata": {},
@@ -1251,7 +1249,7 @@
    ],
    "source": [
     "test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- train\", Xt=vec_train_2, yt=y2, clf=clf_b)\n",
-    "test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- test\", Xt=vec_test_2, yt=yt2, clf=clf_b)\n",
+    "cm_2 = test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- test\", Xt=vec_test_2, yt=yt2, clf=clf_b)\n",
     "test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- valid\", Xt=vectorizer_2.transform(Xv2), yt=yv2, clf=clf_b)"
    ]
   },
@@ -1265,22 +1263,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "'score: 0.4617067833698031'\n",
-      "Confusion matrix, without normalization\n"
+      "'score: 0.5142231947483589'\n",
+      "Confusion matrix, without normalization\n",
+      "array([[  7, 201],\n",
+      "       [ 21, 228]])\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f457feb8eb8>"
+       "<matplotlib.figure.Figure at 0x7f5d5e7375c0>"
       ]
      },
      "metadata": {},
@@ -1292,7 +1292,7 @@
     "yt2_c3[yt2_c3 == \"true\"] = \"REAL\"\n",
     "yt2_c3[yt2_c3 == \"false\"] = \"FAKE\"\n",
     "\n",
-    "test_classifier(labels=[\"REAL\", \"FAKE\"], \n",
+    "cm_3b=test_classifier(labels=[\"REAL\", \"FAKE\"], \n",
     "                title=\"configuration 3: model a) → dataset 2\",\n",
     "                Xt=vectorizer_1.transform(Xt2),\n",
     "                yt=yt2_c3, clf=clf_a)"
@@ -1300,22 +1300,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "'score: 0.4962121212121212'\n",
-      "Confusion matrix, without normalization\n"
+      "'score: 0.5391414141414141'\n",
+      "Confusion matrix, without normalization\n",
+      "array([[274, 491],\n",
+      "       [239, 580]])\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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2Ityg/j+SOsW7SpbLW4DOueQl1AKspL7A42a2ysy+BKZQ+l7eG/AE6JxLVsl5gJkmaCnpvZRpUDlbM+Dfkt4vs3ywpAmS/iZpkzivLeHe0SVmxnkV8i6wcy552XWB55vZnhnW6WFmsyRtDrws6RPCzdqvJyTH6wk3mz+zKmF6C9A5l7DkDoKY2az4/1zgWaC7mc0xs7Vmtg74Cz90c2cB7VNe3i7Oq5AnQOdc8qTMU8ZNqLGkpiWPgUOBjyW1SVntGODj+HgE0F9SQ0lbA9sDY9Ltw7vAzrlkSVCUSGppDTyrkCzrAY+a2YuSHpbUjdAFngacDWBmEyU9CUwC1gDnpzsCXLJR55xLVgKnwZjZF0DXcuafkuY1Q4Gh2e7DE6BzLnl+LbBzrmB5PUDnXEHyeoDOuUImbwE65wqR8ATonCtUilMe8ATonEuYKCryo8DOuQLlXWDnXMHyBOicK0w+BuicK1TyMUDnXCHzLrBzrmB5AnTOFSYfA3TOFSofA3TOFTTvAjvnCld+5D9PgM65hMlbgM65AuZjgM65giSUNy3A/EjTddTXs2ZwYt9DOWjfbhy8324M+/NdAJw38GR69+xO757d2a9bJ3r37F7qdbNmfkXnDpvx57vuyEXYBaFIMOKi/bn/zD0A2Ge7TXnuwv14/tIe3NJ/F4qLwi/4Nq0a84/B+zDppsMY2HPrXIZcuyiLqRbwFmAOFRfX4+rrbmaXrruxdMkSjjh4Xw7oeTD3DBu+fp3rf3sFTZs1K/W6666+gl4HH1bT4RaU0w/oyJQ5S2nSqB4S/L7/rpxy3ximzV/OBYdtz7F7tuUfY2ayaMVqrntuMofstHmuQ649EhwDlDQNWAKsBdaY2Z6SNgWeADoSbot5gpktVNjpH4E+wHLgdDP7IN32vQWYQ623aMMuXXcDoEnTpmy3fWe+mf3DjezNjJH/fIq+x564ft5Lo0bQYauOdOrcpcbjLRRbNG9Ery6teHLMDAA22bgBq9cY0+YvB2D0Z/M5bJfWAHy79Hs+mrGYNessZ/HWRkVFRRmnSjjQzLqZ2Z7x+a+BV8xse+CV+BzgcMLN0LcHBgH3ZoyzMlG46jPjq2lM/Gg8u+3xQ3d3zNtv0rJVa7bedjsAli1dyr133saFl12VqzALwtV9u3DzyE+xmNO+XfY9xcVi53ahJd571y1o02KjHEaYB6q3C9wX+Ht8/HegX8r8hyx4B2ghqU26DdVIApTUQtJ5NbGvfLRs6VLOPv0krhl6a6nu7nNPP0nfn52w/vkdt9zAwHN/SeMmTXIRZkE4sEsrFixdxcRZ35Waf+Hw8Vx1dBee/tW+LFu1hrXe4ktLUsYJaCnpvZRpUDmbMuDfkt5PWd7azGbHx98ArePjtsCMlNfOjPMqVFNjgC2A84B7UmdKqmdma2oohlpp9erVnH16f445rj+HH9Vv/fw1a9bw4qjnGPXKW+vnjXt/DM+PeIbfXfsbvlu8GBUV0bBhI04/69xchF4n7dFxEw7esTU9O7eiYb1imjSqx20n7colj03gpHveBaBHp5Zs3apxjiOtvVISXCbzU7q1FelhZrMkbQ68LOmT1IVmZpKq/NeophLgTcC2ksYDq4GVwEKgs6RDgZFmtjOApEuBJmZ2raRtgbuBVoRBzbPM7JNy95CHzIzLfnU223XqzFnnXVBq2ZtvvMq223eiTdt26+c9PerV9Y9vv/l6Gjdu4skvYbe+8Bm3vvAZAHtvuykDe27NJY9NYNMmDfh26fc0KC5i0IFbc88rU3Mcae2W1HmAZjYr/j9X0rNAd2COpDZmNjt2cefG1WcB7VNe3i7Oq1BNJcBfAzubWTdJvYBR8fmXkjqmed39wDlm9rmkvQktyIPKrhSbxoMA2rZrX3ZxrTX23bd45slH6bzjzutPdbn86us46JDejHjmSY5OOfjhcuusXltzYJfNKRI8+vYM3pnyLQAtmzbgnxfsT5NG9VhnxhkHdKT37//H0lUF3bFJ5DQXSY2BIjNbEh8fClwHjABOIzSsTgOeiy8ZAQyW9DiwN7A4patcrlydBjPGzL5Mt4KkJsB+wD9SmtMNy1vXzO4nJEt27bZH3gzOdN9nf75asLLcZbff/de0r734it9WR0guxbtTv+XdqSHR3TzyU24e+ekG68xf8j09bnitpkOr9RI6DaY18GzcVj3gUTN7UdJY4ElJA4HpQMlA+fOEU2CmEHqMZ2TaQa4S4LKUx2sofTCmUfy/CFhkZt1qLCrn3I+X0HmAZvYF0LWc+QuAg8uZb8D5ldlHTZ0GswRoWsGyOcDmkjaT1BA4EsDMvgO+lHQ8gIINPgznXO0S6gFmnmqDGmkBmtkCSaMlfQysICS9kmWrJV0HjCEMWKYe5BgA3CvpaqA+8DjwYU3E7Jyrujy5FLjmusBm9vM0y+4E7ixn/pdA7+qMyzmXvHwphuDXAjvnkiVvATrnCpSA4uL8yICeAJ1zifMusHOuMHkX2DlXqIS3AJ1zBav2nOeXiSdA51zivAXonCtMPgbonCtUPgbonCtoPgbonCtYedIA9ATonEtYgrfFrG6eAJ1ziQpjgLmOIjueAJ1zCfPzAJ1zBcy7wM65wuTnATrnCpWfB+icK2j5MgZYUzdFcs4VEEkZp0psq1jSOEkj4/MHJX0paXycusX5knSnpCmSJkjaPdO2vQXonEtW8mOAFwCTgWYp8y4zs6fKrHc4sH2c9gbujf9XyFuAzrlEicytv2xbgJLaAUcAf81i9b7AQxa8A7SQ1CbdCzwBOucSV1ykjBPQUtJ7KdOgcjb1B+ByYF2Z+UNjN/eOeD9xgLbAjJR1ZsZ5FaqwCyypWUXLYP2Ny51zbgNZNvDmm9meFW9DRwJzzex9Sb1SFl0JfAM0AO4HrgCuq0qc6cYAJwJGOKpdouS5AR2qskPnXN2m5K4F3h84WlIfoBHQTNJwMzs5Ll8l6QHg0vh8FtA+5fXt4rwKVZgAzax9Rcuccy6dJM6CMbMrCa09YgvwUjM7WVIbM5utkGX7AR/Hl4wABkt6nHDwY7GZzU63j6yOAkvqD2xjZjfGQcnWZvZ+ld6Vc67Oq+bzAB+R1IrQGx0PnBPnPw/0AaYAy4EzMm0oYwKUdBdQH/gJcGPc8H3AXlWJ3DlXt4lwJDhJZvY68Hp8fFAF6xhwfmW2m00LcD8z213SuLiTbyU1qMxOnHOFJU8uBMkqAa6WVEQ48IGkzdjwkLRzzgWVvNIjl7JJgHcDTwOtJA0BTgCGVGtUzrm8JSg5z6/Wy5gAzewhSe8DP42zjjezj9O9xjlX2PKkAZj1tcDFwGpCN9ivHnHOpZUvXeCMyUzSVcBjwJaEEwsflXRldQfmnMtPUnZTbZBNC/BUYDczWw4gaSgwDvhddQbmnMtfxbUlw2WQTQKcXWa9enGec86VK1+6wOmKIdxBGPP7Fpgo6aX4/FBgbM2E55zLN6JunAdYcqR3IjAqZf471ReOcy7vqQ7cFtPMhtVkIM65uiPvu8AlJG0LDAV2JJSkAcDMOlVjXM65PJVPXeBszul7EHiA8L4OB54EnqjGmJxzeS7JmyJVp2wS4MZm9hKAmU01s6sJidA55zYghdNgMk21QTanwayKxRCmSjqHUGG1afWG5ZzLZ7Ukv2WUTQK8CGgM/IowFtgcOLM6g3LO5bfa0sXNJJtiCO/Gh0uAU6o3HOdcXZAn+S/tidDPEmsAlsfMjq2WiJxzeU1SnSiHdVeNRZGg+sWiVbOGmVd0NWrWK6Myr+TqjLzvApvZKzUZiHOu7siXmnnZ1gN0zrmsiPxpAeZLonbO5ZF6RZmnbEkqljRO0sj4fGtJ70qaIumJkpu0SWoYn0+Jyztm2nbWYUjygTXnXEah4GmiV4JcAExOeX4zcIeZbQcsBAbG+QOBhXH+HXG9tLKpCN1d0kfA5/F5V0l/qkz0zrnCUqTMUzYktQOOAP4anws4CHgqrvJ3oF983Dc+Jy4/WBkybTYtwDuBI4EFAGb2IXBgduE75wpRliXxW0p6L2UaVM6m/gBczg+34t0MWGRma+LzmUDb+LgtMAMgLl8c169QNgdBisxseplEujaL1znnCpCAetl1ceeb2Z4Vbkc6EphrZu9L6pVQeKVkkwBnSOoOmKRi4JfAZ9URjHOubkjoIPD+wNGS+hBK8TUD/gi0kFQvtvLaEeoTEP9vD8yUVI9w2e6CdDvIpgt8LnAx0AGYA+wT5znn3AYkUZTFlImZXWlm7cysI9AfeNXMBgCvAcfF1U4DnouPR8TnxOWvmlmFV7NBdtcCz407d865rFTzaYBXAI9LuoFwh8qS6vXDgIclTSHcyyhj3sqmIvRfKOeaYDMrb8DSOVfgBNRL+FpgM3sdeD0+/gLoXs46K4HjK7PdbMYA/5PyuBFwDPFIi3POlSdPLgTJqgtcqvy9pIeBN6stIudcfqvEeX65VpVrgbcGWicdiHOu7hD5kQGzGQNcyA9jgEWEwcVfV2dQzrn8FcYAcx1FdtImwHgZSVd+OM9mXabDys45VyeqwcRk97yZrY2TJz/nXFol9wVO4lrg6pZNQ3W8pN2qPRLnXN2QxXXAtaWBmO6eICWXmuwGjJU0FVhGSPBmZrvXUIzOuTxSHecBVpd0Y4BjgN2Bo2soFudcHVFbWniZpEuAAjCzqTUUi3OuThBFdeA0mFaSLq5ooZndXg3xOOfyXLgnSK6jyE66BFgMNIE8SeXOudpBdWMMcLaZXVdjkTjn6oS60gLMk7fgnKttsqn3VxukS4AH11gUzrk6JU/yX8UJ0My+rclAnHN1gwTFeZIBq1INxjnn0sqP9OcJ0DmXsHAtcH6kQE+AzrnE5Uf68wTonEucKKoD5wE651yliezKTNUG+RKncy6PSMo4ZbGNRpLGSPpQ0kRJQ+L8ByV9KWl8nLrF+ZJ0p6QpkiZIylixyluAzrnEJdQBXgUcZGZLJdUH3pT0Qlx2mZk9VWb9w4Ht47Q3cG/8v0KeAJ1ziUrqPMBYgX5pfFo/Tumq0vcFHoqve0dSC0ltzGx2RS/wLrBzLnFZdoFbSnovZRpUznaKJY0H5gIvm9m7cdHQ2M29Q1LDOK8tpe9ZPjPOq5C3AJ1zicuy/TffzPZMt4KZrQW6SWoBPCtpZ+BK4BugAXA/cAVQpcIt3gJ0ziUu6XuCmNki4DWgt5nNtmAV8ADQPa42C2if8rJ2/HBHy3J5AnTOJUqEMcBMU8btSK1iyw9JGwGHAJ9IahPnCegHfBxfMgI4NR4N3gdYnG78D7wL7JxLnFAyx4HbAH+XVExorD1pZiMlvSqpFSHXjgfOies/D/QBpgDLgTMy7cAToHMucUlcCmxmEwh3pSw7/6AK1jfg/MrswxOgcy5R4UoQvxTOOVeIBEV5cnTBE2AOzZgxg1+ccSpz585BEmcOHMTgX13AkGt+y8gRz1FUVESrzTfn/mEPsuWWW7Jw4ULOPutMvpw6lYaNGvHnv/yNnXbeOddvo076ZNQQlixbxdp161izdh09BtzCrp3a8qer+tOwYX3WrF3HhTc+wXsTpwNw2+XHcdj+O7F85fcMuuZhxn8yM8fvILcSGgOsdnmSp+umevXqcdMttzFuwiTeePMd/nzf3UyeNImLLrmMseMm8O774zm8z5H87oZwitMtN91I167dGDtuAsMeeIhLL74gx++gbus96I/s0/8megy4BYChF/Zj6P0vsE//m7j+3pEMvbAfAIf12JFtO7Ri575DGHzDY9z5m/65DDvnQj3AzFNt4Akwh9q0acNuu4frtZs2bUrnzl34+utZNGvWbP06y5cvW3/h+CeTJ9HzwDD+u0PnzkyfPo05c+bUfOAFygyaNW4EQPMmGzF73mIAjuy5K4+OHAPAmI+m0bzpRmzRslmF2ykEyuJfbeBd4Fpi+rRpjB8/jr26h2u3r/ntVTwy/CGaN2/Oiy+/BsAuu3bluWefoUePAxg7ZgxfTZ/OrJkzad26dS5Dr5PMjH/dMxgzY9jTo/nbM6O57Nan+Nfd5/O7i46hqEgcePptAGy5eQtmfrNw/WtnzVnElpu34Jv53+Uq/JzLl4rQ1dYClPQrSZMlPVLB8l6SRlbX/vPJ0qVLOemEn/H72/6wvvU35Pqfgth+AAAOhklEQVShTPlyBv1PGsB999wFwKWX/5rFixax9x7duPfuP9G1224UFxfnMvQ66+Az7mC/n99Mv8H3cPaJB7D/7tsy6PgDuPy2Z9j+8N9y+a1Pc+81A3IdZq3kXeDgPOAQM/NvSRqrV6/mpBN+xoknDaDfMcdusPzEkwbwz2efBqBZs2bcP+wB3n1/PMMefIj58+ex9Tbb1HTIBeHr2L2dt3ApI16dwF47dWTAkXvzz1fGA/D0y+PYc6etwrpzF9Fui03Wv7Zt6xZ8PXdRzQdda2TTAa4dGbBaEqCk+4BtgBckXSHpbUnjJL0laYdy1u+ZUtxwnKSmcf5lksbGqg9DqiPWXDIzzjlrIDt07sIFF128fv6Uzz9f/3jkiOfotENnABYtWsT3338PwAPD/kqPHj8pNV7okrFxowY02bjh+sc/3bczE6d+zex5izlgj+0B6NW9E1O+mgfAqDc+4udHhstRu+/Ske+Wrijo7i9ZXAdcW3rI1TIGaGbnSOoNHAh8D9xmZmsk/RS4EfhZmZdcCpxvZqMlNQFWSjqUUNiwO6FVPULST8zsv9URcy68NXo0jz7yMDvvvAt779ENgCE33MiDDwzj888+pUhFdNhqK+68+z4APpk8mbMGnoYkuuy4E/fdPyyX4ddZm2/WlCduPwuAesXFPPHCe7z81mTOX/4ov7/sOOrVK2LVqjUMvuExAF58cyKH9diJiSOuYfnK1Zx97fBchp9zJdcC54OaOAjSnHA93/aEYob1y1lnNHB7HC98xsxmxgR4KDAurtOEkBA3SICxjtgggPYdOiT/DqrJ/j16sGL1hvUdex/ep9z199l3Xz6a9Fl1h1Xwps1awN4n3rTB/LfGf8H+8ZSYsi666cnqDiuv5Ef6q5nTYK4HXjOznYGjgEZlVzCzm4BfABsBoyV1JnyGvzOzbnHazszKbfKY2f1mtqeZ7dmqZavqeyfOuewoi6kWqIkE2JwfanKdXt4KkrY1s4/M7GZgLNAZeAk4M3aJkdRW0uY1EK9z7kfKl4MgNdEFvoXQBb4aGFXBOhdKOhBYB0wEXjCzVZK6AG/HE4GXAicTSmM752qx2nKaSybVlgDNrGN8OB/olLLo6rj8deD1+PiXFWzjj8AfqytG51w1KfQE6JwrTGGILz8yoCdA51yyatF5fpl4AnTOJc4ToHOuQNWeo7yZeAJ0ziXOW4DOuYJUi85zzsgLojrnEicp45TFNhpJGiPpQ0kTSwqiSNpa0ruSpkh6QlKDOL9hfD4lLu+YaR+eAJ1ziUuoGswq4CAz6wp0A3rHG57fDNxhZtsBC4GBcf2BwMI4/464XlqeAJ1ziUviUmALlsan9eNkwEHAU3H+34F+8XHf+Jy4/GBlaGp6AnTOJSub7BfSUktJ76VMgzbYlFQsaTzhEtiXganAIjNbE1eZCbSNj9sCMwDi8sXAZulC9YMgzrlEhZL4WfVx55vZnulWMLO1QDdJLYBnCYVSEuMtQOdc4pKuhmVmi4DXgH2BFpJKGm/t+KHa1CygPUBc3hxYkG67ngCdc8lLIANKahVbfkjaCDgEmExIhMfF1U4DnouPR8TnxOWvmtmGFYdTeBfYOZe4hK4EaUMopVdMaKw9aWYjJU0CHpd0A6FifEmh5GHAw5KmAN8CGe9Q7wnQOZe4JOoBmtkEYLdy5n9BuFdQ2fkrgeMrsw9PgM655OXJpSCeAJ1zifJ6gM65wuX1AJ1zhcwToHOuQHk9QOdcAfMWoHOuIOVTPUBPgM65xGVT76828ATonEtcnuQ/T4DOueTlSf7zBOicS5ifB+icK1TCxwCdcwUsP9KfJ0DnXDXIkwagJ0DnXPK8C+ycK1j5kf48ATrnElaJ+/7mnCdA51zivBiCc65geQvQOVewPAE65wpU/tQD9PsCO+cSFa4EyTxl3I7UXtJrkiZJmijpgjj/WkmzJI2PU5+U11wpaYqkTyUdlmkf3gJ0ziUuoS7wGuASM/tAUlPgfUkvx2V3mNmtpfepHQn3At4J2BL4j6ROZra2oh14C9A5lzhl8S8TM5ttZh/Ex0uAyUDbNC/pCzxuZqvM7EtgCuXcPziVJ0DnXLKy6P7GFmJLSe+lTIMq3KTUkXCT9HfjrMGSJkj6m6RN4ry2wIyUl80kfcL0BOicS5aynID5ZrZnynR/uduTmgBPAxea2XfAvcC2QDdgNnBbVWP1MUDnXOKSuhZYUn1C8nvEzJ4BMLM5Kcv/AoyMT2cB7VNe3i7Oq5C3AJ1ziUvoKLCAYcBkM7s9ZX6blNWOAT6Oj0cA/SU1lLQ1sD0wJt0+vAXonEtcQmcB7g+cAnwkaXyc9xvgJEndAAOmAWcDmNlESU8CkwhHkM9PdwQYPAE656pDAhnQzN6sYEvPp3nNUGBotvvwBOicS5SAojy5Fk5mlusYEiVpHjA913EkpCUwP9dBuA3UtZ/LVmbWKqmNSXqR8BllMt/Meie136qocwmwLpH0npntmes4XGn+c6k7/Ciwc65geQJ0zhUsT4C1W7lnxruc859LHeFjgM65guUtQOdcwfIE6JwrWJ4AnXMFyxOgc1WkMiVPyj53tZ8nQOeqQJIsHkGUtBmA+RHFvONHgfOApGOAZUCRmb2Y63jcDyT9EtiXUJjzDeAFM1ud26hctrwFWMtJGgxcCmwKPC3pgByH5CJJxwPHA+cChwI9PPnlF0+AtZSCrYBDgIMI9zZ4A3grVsl1NaxkjE9Sye9NW+AmQlHOr4Gr4vItchKgqzRPgLWXgHmEG7v8H9ATOC4WeDxNUqdcBldoUsf8gJIE9wVwI3CqmR1mZqslXQKck5IkXS3m9QBrIUk9gF3N7B5JGwMDzaxRXPZz4BekKQrpklPS6ks54HEh0E/SEcBUwq0ax0raA+gEDABOMbN1OQrZVYIfBKlFYqtBwEBgD+A14DlgONCCcO+D/YAzzeyjXMVZSCQ1NLNV8fFA4CzgeDObEYciDiD8rHoCK4Dr/GeTPzwB1iKSOpjZV7HVdzzhF+sdM3tU0tHAWsINYr7IaaAFQtJ2hDG+S8xsuqSLCTfbXgl0Bc4E7iH8gVpJ+H1anqt4XeX5OEUtIWlL4H+SDo+/RE8RWnynSToVeN7MRnnyq1HfE7q5v4t3IpsCnAdcDHxFOOjxE6C5ma3w5Jd/PAHWApIuJ5xL9hvgRkmHmtmyeKPoBsCuQJNcxliIzOwr4G7CwY7bCEfhjwN+ZmZPAEsI955dlbMg3Y/iCTDHJPUBegFjzewR4PfA7ZKOiN3eFcBtZrYoh2EWhHjqUanfiZgEbye0+P4MbGpmyySdB9wMDDKz2TUfrUuCjwHWMEkNgO3MbJKk04FfA1PM7MiUdY4DLiEkvwvNbEJOgi0wkpqY2dL4+GygGeHqm5slNQeuADoSusBtgMU+JJHfPAHWsDiwfg/h0qkOwN+AC4G/m9mdKes1B9aY2bKcBFpgYmu7r5kNlHQR0A/4LXAX8JGZDZDUlHDP2Y0JLT8/1SXP+XmANczMpkiaAAwCrjCzhyXNB86O59r+Ka63OKeBFpBYzOBXwGBJOwB7AofHeVOBjSQ9ZWbHSboK2MiTX93gCTA37gM+BC6W9K2ZPSFpLnCPpPlm9liO4ys03wNrgGsAA64EuhNahPtK6g68IGm4mZ1MOPjh6gBPgDlgZlOAKZIWAUPj/40Iv4jv5DS4AmRmSyS9Srjk8NZ4zt/WwNtxlc6Eg1OP5ypGVz08AeaQmf1L0mrgVkK5q4Fm9mWOwypUTwDvA3dJWgC8AOwm6W+E7nBPM5uWw/hcNfCDILWApM0Jl5vOy3UshU7S7oRk+BvgTULFlwX+h6lu8gToXBmSugKvAlfGk9FdHeUJ0LlySNoZWGFmU3Mdi6s+ngCdcwXLL4VzzhUsT4DOuYLlCdA5V7A8ATrnCpYnQOdcwfIEWEdJWitpvKSPJf0jltmv6rZ6SRoZHx8t6ddp1m0Ra+VVdh/XSro02/ll1nkwlhDLdl8dJX1c2Rhd3eMJsO5aYWbdzGxnwjXG56QuLK/4ZzbMbISZ3ZRmlRaEsvHO1XqeAAvD/4DtYsvnU0kPEe430l7SoZLelvRBbCk2AZDUW9Inkj4Aji3ZkKTTJd0VH7eW9KykD+O0H+EmQtvG1ufv43qXSRoraYKkISnbukrSZ5LeBHbI9CYknRW386Gkp8u0an8q6b24vSPj+sWSfp+y77N/7Afp6hZPgHWcpHqEi/lLbtW4PXCPme1EKMBwNfBTM9sdeI9QoqsR8BfgKMKd6bbYYMPBncAbZtYV2B2YSKhwPTW2Pi+TdGjcZ3egG7CHpJ8o3Ee3f5zXB9gri7fzjJntFfc3mXD70BId4z6OAO6L72EgoWrzXnH7Z8UqL84BXg2mLttI0vj4+H/AMGBLYLqZlZTc2gfYERitcP/vBoQSUJ2BL83scwBJwwkFXMs6CDgVwMzWAoslbVJmnUPjNC4+b0JIiE2BZ0vupCZpRBbvaWdJNxC62U2Al1KWPRmLlH4u6Yv4Hg4Fdk0ZH2we9/1ZFvtyBcATYN21wsy6pc6ISS61xL6Al83spDLrlXrdjyTgd2b25zL7uLAK23oQ6GdmH8b7qfRKWVb2mk6L+/6lmaUmSiR1rMK+XR3kXeDC9g6wf7xPCZIaS+oEfAJ0lLRtXO+kCl7/CnBufG1xvI/JEkLrrsRLwJkpY4ttY/mv/wL9JG0U77VxVBbxNgVmS6oPDCiz7HhJRTHmbYBP477PjesjqZOkxlnsxxUIbwEWMDObF1tSj0lqGGdfbWafSRoEjJK0nNCFblrOJi4A7pc0EFgLnGtmb0saHU8zeSGOA3YB3o4t0KXAyWb2gaQnCLcGmAuMzSLk3wLvAvPi/6kxfQWMIdzJ7RwzWynpr4SxwQ8Udj6PcLMj5wCvBuOcK2DeBXbOFSxPgM65guUJ0DlXsDwBOucKlidA51zB8gTonCtYngCdcwXr/wFZyiJzo91pCQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f457fe43978>"
+       "<matplotlib.figure.Figure at 0x7f5d5e71c320>"
       ]
      },
      "metadata": {},
@@ -1327,7 +1329,7 @@
     "yt1_c3[yt1_c3 == \"REAL\"] = \"true\"\n",
     "yt1_c3[yt1_c3 == \"FAKE\"] = \"false\"\n",
     "\n",
-    "test_classifier(labels=[\"true\", \"false\"], \n",
+    "cm_3a=test_classifier(labels=[\"true\", \"false\"], \n",
     "                title=\"configuration 3: model b) → dataset 1\",\n",
     "                Xt=vectorizer_2.transform(Xt1),\n",
     "                yt=yt1_c3, clf=clf_b)"
@@ -1343,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1378,14 +1380,14 @@
     "\n",
     "        print(df3['y'].value_counts())\n",
     "        print('done')\n",
-    "        return train_test_split(df3['claim'], df3['y'], test_size=0.25, random_state=4222)\n",
+    "        return train_test_split(df3['claim'], df3['y'], test_size=0.3, random_state=4222)\n",
     "    except Exception as e:\n",
     "        print(e)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1402,7 +1404,7 @@
       "6335\n",
       "-- liar liar\n",
       "Index(['y', 'claim'], dtype='object')\n",
-      "{'mostly-true', 'pants-fire', 'half-true', 'barely-true', 'false', 'true'} 10240\n",
+      "{'pants-fire', 'false', 'mostly-true', 'true', 'barely-true', 'half-true'} 10240\n",
       "1676\n",
       "1995\n",
       "{'true', 'false'} 3671\n",
@@ -1419,75 +1421,159 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "vectorizer_3 = CountVectorizer(stop_words='english')\n",
+    "vectorizer_3 = TfidfVectorizer(stop_words='english', max_df=0.7)\n",
     "vec_train_3 = vectorizer_3.fit_transform(X3)\n",
     "vec_test_3 = vectorizer_3.transform(Xt3)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 29,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "clf_3 = MultinomialNB()\n",
+    "* using MLP again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf_3 = MLPClassifier(hidden_layer_sizes=(16,16), random_state=4222)\n",
     "clf_3.fit(vec_train_3, y3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "'score: 0.8030383795309168'\n",
-      "Confusion matrix, without normalization\n",
-      "'score: 0.746203037569944'\n",
-      "Confusion matrix, without normalization\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f457f9a8e10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f457fa31cf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_classifier(labels=[\"true\",\"false\"], title=\"Configuration 4 -- train\", Xt=vec_train_3, yt=y3, clf=clf_3)\n",
-    "test_classifier(labels=[\"true\",\"false\"], title=\"Configuration 4 -- test\", Xt=vec_test_3, yt=yt3, clf=clf_3)"
+    "cm_4=test_classifier(labels=[\"true\",\"false\"], title=\"Configuration 4 -- test\", Xt=vec_test_3, yt=yt3, clf=clf_3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### exporting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recall(cm):\n",
+    "    return cm[0,0] / (cm[0,0] + cm[0,1])\n",
+    "def precision(cm):\n",
+    "    return cm[0,0] / (cm[0,0] + cm[1,0])\n",
+    "\n",
+    "def accuracy(cm):\n",
+    "    return (cm[0,0] + cm[1,1]) / np.sum(cm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rdflib import Namespace, Graph, Literal\n",
+    "from rdflib.namespace import DCTERMS, RDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class mexcore_graph(object):\n",
+    "    def __init__(self, name_exp):\n",
+    "        self.nsp = {\n",
+    "            \"this\": Namespace(\"http://mex.aksw.org/examples/\"),\n",
+    "            \"xsd\": Namespace(\"http://www.w3.org/2001/XMLSchema#\"),\n",
+    "            \"rdfs\": Namespace(\"http://www.w3.org/2000/01/rdf-schema#\"),\n",
+    "            \"mexcore\": Namespace(\"http://mex.aksw.org/mex-core#\"),\n",
+    "            \"mexperf\": Namespace(\"http://mex.aksw.org/mex-perf#\"),\n",
+    "            \"mexalgo\": Namespace(\"http://mex.aksw.org/mex-algo#\"),\n",
+    "            \"prov\": Namespace(\"http://www.w3.org/ns/prov#\"),\n",
+    "            \"dct\": DCTERMS,\n",
+    "            \"rdf\": RDF\n",
+    "        }\n",
+    "        self.g = Graph()\n",
+    "\n",
+    "        for i in nsp.items():\n",
+    "            self.g.bind(i[0],i[1])\n",
+    "        \n",
+    "        self.exp = self.nsp[\"this\"][name_exp]\n",
+    "        self.g.add((self.nsp[\"this\"][name_exp], self.nsp[\"rdf\"].type, self.nsp[\"mexcore\"].Experiment))\n",
+    "        \n",
+    "    def add_dataset(self,dset_name):\n",
+    "        self.g.add((self.nsp[\"this\"][dset_name], self.nsp[\"rdf\"].type, self.nsp[\"mexcore\"].dataset))\n",
+    "        self.g.add((self.nsp[\"this\"][dset_name], self.nsp[\"rdfs\"].label, Literal(dset_name)))\n",
+    "    \n",
+    "            \n",
+    "    def add_datasets(self, dset_names):\n",
+    "        for dset_name in dset_names:\n",
+    "            self.add_dataset(dset_name)\n",
+    "    \n",
+    "    def add_model(self, model_name, literal):\n",
+    "        self.g.add((self.nsp[\"this\"][model_name],self.nsp[\"rdf\"].type,self.nsp[\"mexalgo\"].Algorithm))\n",
+    "        self.g.add((self.nsp[\"this\"][model_name],self.nsp[\"rdfs\"].label,Literal(model_name)))\n",
+    "        self.g.add((self.nsp[\"this\"][model_name],self.nsp[\"dct\"].identifier,Literal(literal)))\n",
+    "    \n",
+    "    def add_configuration(self, conf_name, used_model, used_dset):\n",
+    "        self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"rdf\"].type,self.nsp[\"mexcore\"].ExperimentConfiguration))\n",
+    "        self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"rdfs\"].label,Literal(conf_name)))\n",
+    "        self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"prov\"].used, self.nsp[\"this\"][used_model]))\n",
+    "        self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"prov\"].used, self.nsp[\"this\"][used_dset]))\n",
+    "        self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"prov\"].wasStartedBy, self.exp))\n",
+    "    \n",
+    "    def add_measurement(self, meas_name, used_conf, confusion_matrix):\n",
+    "        self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"rdf\"].type,self.nsp[\"mexcore\"].PerformanceMeasure))\n",
+    "        self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"mexperf\"].precision,Literal(str(precision(confusion_matrix)),datatype=self.nsp[\"xsd\"].float)))\n",
+    "        self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"mexperf\"].recall,Literal(str(recall(confusion_matrix)),datatype=self.nsp[\"xsd\"].float)))\n",
+    "        self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"mexperf\"].accuracy,Literal(str(accuracy(confusion_matrix)),datatype=self.nsp[\"xsd\"].float)))\n",
+    "        self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"prov\"].wasGeneratedBy,self.nsp[\"this\"][used_conf]))\n",
+    "\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mg = mexcore_graph(\"jonas_weinz_task_2\")\n",
+    "mg.add_datasets([\"Dataset01\",\"Dataset02\",\"Dataset03\"])\n",
+    "\n",
+    "mg.add_model(\"model_a\", \"RandomForestClassifier\")\n",
+    "mg.add_model(\"model_b\", \"MLPClassifier\")\n",
+    "mg.add_model(\"model_c\", \"MLPClassifier\")\n",
+    "\n",
+    "mg.add_configuration(\"conf1\",\"model_a\",\"Dataset01\")\n",
+    "mg.add_configuration(\"conf2\",\"model_b\", \"Dataset02\")\n",
+    "mg.add_configuration(\"conf3a\",\"model_b\", \"Dataset01\")\n",
+    "mg.add_configuration(\"conf3b\", \"model_a\", \"Dataset02\")\n",
+    "mg.add_configuration(\"conf4\", \"model_c\", \"Dataset03\")\n",
+    "\n",
+    "mg.add_measurement(\"measure1\", \"conf1\", cm_1)\n",
+    "mg.add_measurement(\"measure2\", \"conf2\", cm_2)\n",
+    "mg.add_measurement(\"measure3a\", \"conf3a\", cm_3a)\n",
+    "mg.add_measurement(\"measure3b\", \"conf3b\", cm_3b)\n",
+    "mg.add_measurement(\"measure4\", \"conf4\", cm_4)\n",
+    "\n",
+    "mg.g.serialize(destination='output.ttl', format='turtle')"
    ]
   },
   {
diff --git a/Jonas_Solutions/Task_03.ipynb b/Jonas_Solutions/Task_03.ipynb
new file mode 100644
index 0000000..af6ec4e
--- /dev/null
+++ b/Jonas_Solutions/Task_03.ipynb
@@ -0,0 +1,316 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jonas/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n",
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np \n",
+    "import pandas as pd \n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "from keras.preprocessing.text import Tokenizer\n",
+    "from keras.preprocessing.sequence import pad_sequences\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "import re\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ">**Task 3**: playing with NN framwork/keras and basic sentiment analysis\n",
+    ">- use the following model as a baseline and improve it!\n",
+    ">- export your metadata (just basic hyperparameters and outcomes for test data!)\n",
+    ">- test data = 0.3 (not in this example, change it!)\n",
+    ">- random_state = 4222\n",
+    ">- no need to cross-validation!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dataset already downloaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "if [ ! -e 'dataset_sentiment.csv' ]\n",
+    "then\n",
+    "    echo \"downloading dataset\"\n",
+    "    wget https://raw.githubusercontent.com/SmartDataAnalytics/MA-INF-4222-NLP-Lab/master/2018_SoSe/exercises/dataset_sentiment.csv\n",
+    "else\n",
+    "    echo \"dataset already downloaded\"\n",
+    "fi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# parameters\n",
+    "max_fatures = 500\n",
+    "embed_dim = 128\n",
+    "lstm_out = 196\n",
+    "dropout = 0.1\n",
+    "dropout_1d = 0.4\n",
+    "recurrent_dropout = 0.1\n",
+    "random_state = 1324\n",
+    "validation_size = 1000\n",
+    "batch_size = 16\n",
+    "epochs=2\n",
+    "verbose= 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                text sentiment\n",
+      "0  RT @NancyLeeGrahn: How did everyone feel about...   Neutral\n",
+      "1  RT @ScottWalker: Didn't catch the full #GOPdeb...  Positive\n",
+      "2  RT @TJMShow: No mention of Tamir Rice and the ...   Neutral\n",
+      "3  RT @RobGeorge: That Carly Fiorina is trending ...  Positive\n",
+      "4  RT @DanScavino: #GOPDebate w/ @realDonaldTrump...  Positive\n",
+      "5  RT @GregAbbott_TX: @TedCruz: \"On my first day ...  Positive\n",
+      "6  RT @warriorwoman91: I liked her and was happy ...  Negative\n",
+      "7  Going on #MSNBC Live with @ThomasARoberts arou...   Neutral\n",
+      "8  Deer in the headlights RT @lizzwinstead: Ben C...  Negative\n",
+      "9  RT @NancyOsborne180: Last night's debate prove...  Negative\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('dataset_sentiment.csv')\n",
+    "df = df[['text','sentiment']]\n",
+    "print(df[0:10])\n",
+    "\n",
+    "df = df[df.sentiment != \"Neutral\"]\n",
+    "df['text'] = df['text'].apply(lambda x: x.lower())\n",
+    "df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))\n",
+    "df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-Z0-9\\s]','',x)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tok = Tokenizer(num_words=max_fatures, split=' ')\n",
+    "tok.fit_on_texts(df['text'].values)\n",
+    "X = tok.texts_to_sequences(df['text'].values)\n",
+    "X = pad_sequences(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "embedding_2 (Embedding)      (None, 26, 128)           64000     \n",
+      "_________________________________________________________________\n",
+      "spatial_dropout1d_2 (Spatial (None, 26, 128)           0         \n",
+      "_________________________________________________________________\n",
+      "lstm_2 (LSTM)                (None, 196)               254800    \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 2)                 394       \n",
+      "=================================================================\n",
+      "Total params: 319,194\n",
+      "Trainable params: 319,194\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "nn = Sequential()\n",
+    "nn.add(Embedding(max_fatures, embed_dim, input_length = X.shape[1]))\n",
+    "nn.add(SpatialDropout1D(dropout_1d))\n",
+    "nn.add(LSTM(lstm_out, dropout=dropout, recurrent_dropout=recurrent_dropout))\n",
+    "nn.add(Dense(2, activation='softmax'))\n",
+    "nn.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics = ['accuracy'])\n",
+    "print(nn.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2\n",
+      " - 21s - loss: 0.4322 - acc: 0.8196\n",
+      "Epoch 2/2\n",
+      " - 25s - loss: 0.3612 - acc: 0.8509\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7f0646b9e828>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y = pd.get_dummies(df['sentiment']).values\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.30, random_state = random_state)\n",
+    "nn.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, verbose=verbose)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "score: 0.37\n",
+      "acc: 0.84\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_validate = X_test[-validation_size:]\n",
+    "Y_validate = Y_test[-validation_size:]\n",
+    "X_test = X_test[:-validation_size]\n",
+    "Y_test = Y_test[:-validation_size]\n",
+    "score, accuracy = nn.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)\n",
+    "print(\"score: %.2f\" % (score))\n",
+    "print(\"acc: %.2f\" % (accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pos_cnt, neg_cnt, pos_ok, neg_ok = 0, 0, 0, 0\n",
+    "for x in range(len(X_validate)):\n",
+    "    result = nn.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]\n",
+    "    if np.argmax(result) == np.argmax(Y_validate[x]):\n",
+    "        if np.argmax(Y_validate[x]) == 0: neg_ok += 1\n",
+    "        else: pos_ok += 1\n",
+    "    if np.argmax(Y_validate[x]) == 0: neg_cnt += 1\n",
+    "    else: pos_cnt += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pos_acc 39.58333333333333 %\n",
+      "neg_acc 95.29702970297029 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"pos_acc\", pos_ok/pos_cnt*100, \"%\")\n",
+    "print(\"neg_acc\", neg_ok/neg_cnt*100, \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  48  37\n",
+      "  311 189   4 144  22  16   1 281]]\n",
+      "[0.8364928  0.16350722]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X2 = ['what are u going to say about that? the truth, wassock?!']\n",
+    "X2 = tok.texts_to_sequences(X2)\n",
+    "X2 = pad_sequences(X2, maxlen=26, dtype='int32', value=0)\n",
+    "print(X2)\n",
+    "print(nn.predict(X2, batch_size=1, verbose = 2)[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Jonas_Solutions/output.ttl b/Jonas_Solutions/output.ttl
new file mode 100644
index 0000000..41dfcb2
--- /dev/null
+++ b/Jonas_Solutions/output.ttl
@@ -0,0 +1,94 @@
+@prefix dct: <http://purl.org/dc/terms/> .
+@prefix mexalgo: <http://mex.aksw.org/mex-algo#> .
+@prefix mexcore: <http://mex.aksw.org/mex-core#> .
+@prefix mexperf: <http://mex.aksw.org/mex-perf#> .
+@prefix prov: <http://www.w3.org/ns/prov#> .
+@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
+@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
+@prefix this: <http://mex.aksw.org/examples/> .
+@prefix xml: <http://www.w3.org/XML/1998/namespace> .
+@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
+
+this:measure1 a mexcore:PerformanceMeasure ;
+    mexperf:accuracy "0.8478535353535354"^^xsd:float ;
+    mexperf:precision "0.8225446428571429"^^xsd:float ;
+    mexperf:recall "0.8998778998778999"^^xsd:float ;
+    prov:wasGeneratedBy this:conf1 .
+
+this:measure2 a mexcore:PerformanceMeasure ;
+    mexperf:accuracy "0.5776805251641138"^^xsd:float ;
+    mexperf:precision "0.5369458128078818"^^xsd:float ;
+    mexperf:recall "0.5240384615384616"^^xsd:float ;
+    prov:wasGeneratedBy this:conf2 .
+
+this:measure3a a mexcore:PerformanceMeasure ;
+    mexperf:accuracy "0.5391414141414141"^^xsd:float ;
+    mexperf:precision "0.5341130604288499"^^xsd:float ;
+    mexperf:recall "0.3581699346405229"^^xsd:float ;
+    prov:wasGeneratedBy this:conf3a .
+
+this:measure3b a mexcore:PerformanceMeasure ;
+    mexperf:accuracy "0.5142231947483589"^^xsd:float ;
+    mexperf:precision "0.25"^^xsd:float ;
+    mexperf:recall "0.03365384615384615"^^xsd:float ;
+    prov:wasGeneratedBy this:conf3b .
+
+this:measure4 a mexcore:PerformanceMeasure ;
+    mexperf:accuracy "0.7714856762158561"^^xsd:float ;
+    mexperf:precision "0.7680865449628127"^^xsd:float ;
+    mexperf:recall "0.7680865449628127"^^xsd:float ;
+    prov:wasGeneratedBy this:conf4 .
+
+this:Dataset03 a mexcore:dataset ;
+    rdfs:label "Dataset03" .
+
+this:conf1 a mexcore:ExperimentConfiguration ;
+    rdfs:label "conf1" ;
+    prov:used this:Dataset01,
+        this:model_a ;
+    prov:wasStartedBy this:jonas_weinz_task_2 .
+
+this:conf2 a mexcore:ExperimentConfiguration ;
+    rdfs:label "conf2" ;
+    prov:used this:Dataset02,
+        this:model_b ;
+    prov:wasStartedBy this:jonas_weinz_task_2 .
+
+this:conf3a a mexcore:ExperimentConfiguration ;
+    rdfs:label "conf3a" ;
+    prov:used this:Dataset01,
+        this:model_b ;
+    prov:wasStartedBy this:jonas_weinz_task_2 .
+
+this:conf3b a mexcore:ExperimentConfiguration ;
+    rdfs:label "conf3b" ;
+    prov:used this:Dataset02,
+        this:model_a ;
+    prov:wasStartedBy this:jonas_weinz_task_2 .
+
+this:conf4 a mexcore:ExperimentConfiguration ;
+    rdfs:label "conf4" ;
+    prov:used this:Dataset03,
+        this:model_c ;
+    prov:wasStartedBy this:jonas_weinz_task_2 .
+
+this:model_c a mexalgo:Algorithm ;
+    rdfs:label "model_c" ;
+    dct:identifier "MLPClassifier" .
+
+this:Dataset01 a mexcore:dataset ;
+    rdfs:label "Dataset01" .
+
+this:Dataset02 a mexcore:dataset ;
+    rdfs:label "Dataset02" .
+
+this:model_a a mexalgo:Algorithm ;
+    rdfs:label "model_a" ;
+    dct:identifier "RandomForestClassifier" .
+
+this:model_b a mexalgo:Algorithm ;
+    rdfs:label "model_b" ;
+    dct:identifier "MLPClassifier" .
+
+this:jonas_weinz_task_2 a mexcore:Experiment .
+
diff --git a/Project/Tools/emoji tester old.ipynb b/Project/Tools/emoji tester old.ipynb
new file mode 100644
index 0000000..054473e
--- /dev/null
+++ b/Project/Tools/emoji tester old.ipynb	
@@ -0,0 +1,423 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import clear_output, Markdown, Math\n",
+    "import ipywidgets as widgets\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## file input stuff:\n",
+    "\n",
+    "* replace `test.txt` with yout whatsapp log file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "./whatsapp2csv.sh test.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* read table `test.csv` exported by `whatsapp2csv.sh`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = pd.read_csv('test.txt.csv', delimiter='\\t')\n",
+    "messages.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* read emoji-data (can be found here: https://www.unicode.org/Public/emoji/11.0/emoji-data.txt) and generate a table file out of it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "if [ ! -e emoji-data.txt ]\n",
+    "then\n",
+    "    echo \"downloading emoji specification\"\n",
+    "    wget https://www.unicode.org/Public/emoji/11.0/emoji-data.txt\n",
+    "else\n",
+    "    echo \"found existing emoji specification\"\n",
+    "fi\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emoji_blacklist = set([\n",
+    "    0x1F3FB,\n",
+    "    0x1F3FC,\n",
+    "    0x1F3FD,\n",
+    "    0x1F3FE,\n",
+    "    0x1F3FF\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emoji_data = pd.read_csv('emoji-data.txt', delimiter=';', comment='#', names=[\"unicode\",\"type\"])\n",
+    "emoji_data['type'] = emoji_data['type'].str.strip()\n",
+    "emoji_data = emoji_data[emoji_data['type'] == \"Emoji_Presentation\"]\n",
+    "emoji_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* now build a set out of the unicode types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ord(\"😀\") == int('0x1f600',16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emoji_codes = emoji_data['unicode']\n",
+    "emoji_codes.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* we have to iterate over the whole list and extract all given ranges:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emoji_code_list = []\n",
+    "for entry in emoji_codes:\n",
+    "    # testing whether we have an entry or a range:\n",
+    "    if '.' in entry:\n",
+    "        # range\n",
+    "        a,b = entry.split(\"..\")\n",
+    "        for i in range(int(a,16),int(b,16) +1):\n",
+    "            if i not in emoji_blacklist:\n",
+    "                emoji_code_list.append(i)\n",
+    "    else:\n",
+    "        # single entry\n",
+    "        if i not in emoji_blacklist:\n",
+    "            emoji_code_list.append(int(entry,16))\n",
+    "emoji_code_set = set(emoji_code_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple test:\n",
+    "print(ord(\"😀\") in emoji_code_set, ord(\"a\") in emoji_code_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* expanding column and fill new emojis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages[\"emojis\"] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in messages.index:\n",
+    "    emoji_list = []\n",
+    "    to_remove = []\n",
+    "    m = messages.iloc[i]['message']\n",
+    "    for c in str(m):\n",
+    "        if ord(c) in emoji_code_set:\n",
+    "            emoji_list.append(c)\n",
+    "        elif ord(c) in emoji_blacklist:\n",
+    "            to_remove.append(c)\n",
+    "            \n",
+    "    messages.loc[i,'emojis'] = emoji_list\n",
+    "    #remove emiójis from message\n",
+    "    for e in (emoji_list + to_remove):\n",
+    "        m = m.replace(e,\"\")\n",
+    "    messages.loc[i,'message'] = m\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages[:20]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* get a list only containing messaged with emojis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emoji_messages = messages[[True if len(e) > 0 else False for e in messages['emojis']]]\n",
+    "emoji_messages = emoji_messages[emoji_messages['message'] != \"\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(emoji_messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## learning part"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import itertools\n",
+    "import sklearn.utils as sku\n",
+    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels=[e[-1] for e in emoji_messages['emojis']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X1, Xt1, y1, yt1 = train_test_split(emoji_messages['message'], labels, test_size=0.1, random_state=4222)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectorizer = TfidfVectorizer(stop_words='english')\n",
+    "vec_train = vectorizer.fit_transform(X1)\n",
+    "vec_test = vectorizer.transform(Xt1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier as RFC\n",
+    "from sklearn.neural_network import MLPClassifier as MLP\n",
+    "from sklearn.naive_bayes import MultinomialNB as MNB\n",
+    "#clf_a = RFC(criterion='entropy', random_state=4222)\n",
+    "clf_a = MLP()\n",
+    "clf_a.fit(vec_train, y1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = clf_a.predict(vectorizer.transform(Xt1))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "testlist = pd.DataFrame({'message': Xt1, 'pred': pred, 'trained': yt1})\n",
+    "testlist = pd.merge(testlist, emoji_messages['emojis'].to_frame(), left_index=True, right_index=True)\n",
+    "testlist.to_csv('export.csv')\n",
+    "testlist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(clf_a.predict(vectorizer.transform([\"Boah Caner\"]))[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(chr(0x1F3F))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vec_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "out = widgets.Output()\n",
+    "\n",
+    "t = widgets.Text()\n",
+    "b = widgets.Button(\n",
+    "    description='get smiley',\n",
+    "    disabled=False,\n",
+    "    button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
+    "    tooltip='Click me',\n",
+    "    icon='check'\n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "def handle_submit(sender):\n",
+    "    with out:\n",
+    "        clear_output()\n",
+    "    with out:\n",
+    "        display(Markdown(\"# \" + str(clf_a.predict(vectorizer.transform([t.value]))[0])))\n",
+    "\n",
+    "b.on_click(handle_submit)\n",
+    "    \n",
+    "display(t)\n",
+    "display(widgets.VBox([b, out]))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Project/Tools/emoji tester.ipynb b/Project/Tools/emoji tester.ipynb
new file mode 100644
index 0000000..e47d039
--- /dev/null
+++ b/Project/Tools/emoji tester.ipynb	
@@ -0,0 +1,676 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import clear_output, Markdown, Math\n",
+    "import ipywidgets as widgets\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_widgets(t_text, b_text, out, additional_widgets=[]):\n",
+    "    texts = []\n",
+    "    for t in t_text:\n",
+    "        texts.append(widgets.Text(t))\n",
+    "    \n",
+    "    button = widgets.Button(\n",
+    "        description=b_text,\n",
+    "        disabled=False,\n",
+    "        button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
+    "        tooltip=b_text,\n",
+    "        icon='check'\n",
+    "    )\n",
+    "    display(widgets.VBox([widgets.HBox(texts + additional_widgets + [button]), out]))\n",
+    "    return texts + [button]\n",
+    "\n",
+    "out_convert = widgets.Output()\n",
+    "out_build = widgets.Output()\n",
+    "out_train = widgets.Output()\n",
+    "out_save = widgets.Output()\n",
+    "out_read = widgets.Output()\n",
+    "out_test = widgets.Output()\n",
+    "\n",
+    "def mp(msg):\n",
+    "    display(Markdown(msg))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Emoji Tester\n",
+    "\n",
+    "just run all cells at first. Then select on of the actions below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "## converting plain whatsapp export to csv"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c94a8d3b3724ad08359817b2086cf85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "VBox(children=(HBox(children=(Text(value='test.txt'), Button(description='convert whatsapp file to csv', icon='check', style=ButtonStyle(), tooltip='convert whatsapp file to csv'))), Output()))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "## read csv and build database"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee22e300097e49f1ac24b11662f7dc69",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "VBox(children=(HBox(children=(Text(value='test.txt.csv'), Checkbox(value=False, description='using only last emoji'), Button(description='read', icon='check', style=ButtonStyle(), tooltip='read'))), Output()))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "## Train"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de06136ee346492d80ef39d304cbc31c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "VBox(children=(HBox(children=(Dropdown(description='Learning Method', index=1, options=('DecisionTree', 'MLP', 'RandomForest'), value='MLP'), Checkbox(value=False, description='Using one vs all (very slow, only with multi-label!)'), Button(description='train', icon='check', style=ButtonStyle(), tooltip='train'))), Output()))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "## save trained classifier"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b046f3ada7ec4ba7a17965d718552d21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "VBox(children=(HBox(children=(Text(value='clf.pkl'), Text(value='mlb.pkl'), Text(value='vectorizer.pkl'), Button(description='save classifier', icon='check', style=ButtonStyle(), tooltip='save classifier'))), Output()))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "## import trained classifier"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac4054e75d8a4214ad040b1b21d6c925",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "VBox(children=(HBox(children=(Text(value='clf.pkl'), Text(value='mlb.pkl'), Text(value='vectorizer.pkl'), Button(description='import classifier', icon='check', style=ButtonStyle(), tooltip='import classifier'))), Output()))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "## predict emoji on custom text"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db631acbe9c94cac907efaf501a69c6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "VBox(children=(HBox(children=(Text(value=''), Checkbox(value=False, description='Show probabilities (only on trees)'), Button(description='get emoji', icon='check', style=ButtonStyle(), tooltip='get emoji'))), Output()))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mp(\"## converting plain whatsapp export to csv\")\n",
+    "t_convert, b_convert = create_widgets([\"test.txt\"], \"convert whatsapp file to csv\", out_convert)\n",
+    "mp(\"## read csv and build database\")\n",
+    "single_label = widgets.Checkbox(value=False, description='using only last emoji', disable=False)\n",
+    "t_build, b_build = create_widgets([\"test.txt.csv\"], \"read\", out_build, [single_label])\n",
+    "mp(\"## Train\")\n",
+    "d = widgets.Dropdown(options=['DecisionTree', 'MLP', 'RandomForest'], value='MLP', description='Learning Method', disabled=False)\n",
+    "ova = widgets.Checkbox(value=False, description='Using one vs all (very slow, only with multi-label!)', disabled=False)\n",
+    "b_train = button = widgets.Button(description=\"train\", disabled=False, button_style='', tooltip=\"train\",icon='check')\n",
+    "display(widgets.VBox([widgets.HBox([d,ova,b_train]), out_train]))\n",
+    "mp(\"## save trained classifier\")\n",
+    "t_save_c, t_save_m, t_save_v, b_save = create_widgets([\"clf.pkl\", \"mlb.pkl\", \"vectorizer.pkl\"], \"save classifier\", out_save)\n",
+    "mp(\"## import trained classifier\")\n",
+    "t_read_c, t_read_m, t_read_v, b_read = create_widgets([\"clf.pkl\", \"mlb.pkl\", \"vectorizer.pkl\"], \"import classifier\", out_read)\n",
+    "mp(\"## predict emoji on custom text\")\n",
+    "b_prop = widgets.Checkbox(value=False, description='Show probabilities (only on trees)', disabled=False)\n",
+    "t_test, b_test = create_widgets([\"\"], \"get emoji\", out_test,[b_prop])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## Code Section:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert(b):\n",
+    "    with out_convert:\n",
+    "        clear_output()\n",
+    "    with out_convert:\n",
+    "        mp(\"**converting \" + t_convert.value + \"…**\")\n",
+    "        import subprocess\n",
+    "        print(str(subprocess.check_output([\"./whatsapp2csv.sh\", t_convert.value])).strip())\n",
+    "        mp(\"**done**\")\n",
+    "\n",
+    "b_convert.on_click(convert)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* download emoji specification if not already existing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "found existing emoji specification\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "if [ ! -e emoji-data.txt ]\n",
+    "then\n",
+    "    echo \"downloading emoji specification\"\n",
+    "    wget https://www.unicode.org/Public/emoji/11.0/emoji-data.txt\n",
+    "else\n",
+    "    echo \"found existing emoji specification\"\n",
+    "fi\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* stuff for creating emoji database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emoji_blacklist = set([\n",
+    "    0x1F3FB,\n",
+    "    0x1F3FC,\n",
+    "    0x1F3FD,\n",
+    "    0x1F3FE,\n",
+    "    0x1F3FF,\n",
+    "    0x2642,\n",
+    "    0x2640\n",
+    "])\n",
+    "\n",
+    "emoji_code_set = None\n",
+    "\n",
+    "def create_emoji_set():\n",
+    "    global emoji_code_set\n",
+    "    \n",
+    "    emoji_data = pd.read_csv('emoji-data.txt', delimiter=';', comment='#', names=[\"unicode\",\"type\"])\n",
+    "    emoji_data['type'] = emoji_data['type'].str.strip()\n",
+    "    emoji_data = emoji_data[emoji_data['type'] == \"Emoji_Presentation\"]\n",
+    "    \n",
+    "    emoji_codes = emoji_data['unicode']\n",
+    "    emoji_codes.head()\n",
+    "    \n",
+    "    emoji_code_list = []\n",
+    "    for entry in emoji_codes:\n",
+    "        # testing whether we have an entry or a range:\n",
+    "        if '.' in entry:\n",
+    "            # range\n",
+    "            a,b = entry.split(\"..\")\n",
+    "            for i in range(int(a,16),int(b,16) +1):\n",
+    "                if i not in emoji_blacklist:\n",
+    "                    emoji_code_list.append(i)\n",
+    "        else:\n",
+    "            # single entry\n",
+    "            if i not in emoji_blacklist:\n",
+    "                emoji_code_list.append(int(entry,16))\n",
+    "    emoji_code_set = set(emoji_code_list)\n",
+    "    display(Markdown(\"**imported Emojis** (without modifier):\\n>\" + \"\".join([chr(x) for x in emoji_code_set])))\n",
+    "    display(Markdown(\"**blacklisted Emojis:**\\n>\" + \"\".join([chr(x) for x in emoji_blacklist])))\n",
+    "    f = open('emoji-list.txt', 'w')\n",
+    "    for e in emoji_code_set:\n",
+    "        f.write(chr(e) + \"\\n\")\n",
+    "    f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* stuff for reading whatsapp messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = None\n",
+    "vectorizer = None\n",
+    "clf_a = None\n",
+    "mlb = None\n",
+    "\n",
+    "emoji_messages=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_message_and_build_db(filename):\n",
+    "    global messages\n",
+    "    global emoji_messages\n",
+    "    global vectorizer\n",
+    "    global clf_a\n",
+    "    global mlb\n",
+    "    \n",
+    "    messages = pd.read_csv(filename, delimiter='\\t')\n",
+    "    mp(\"**filter messages and creating labels. This can take a while...**\")\n",
+    "    messages[\"emojis\"] = None\n",
+    "    \n",
+    "    msg_batchsize = 1000\n",
+    "    msg_counter = 0\n",
+    "    \n",
+    "    for i in messages.index:\n",
+    "        \n",
+    "        msg_counter+=1\n",
+    "        if msg_counter >= msg_batchsize:\n",
+    "            print(str(100 * i / messages.shape[0]) + \"%\")\n",
+    "            msg_counter=0\n",
+    "        \n",
+    "        emoji_list = []\n",
+    "        m = messages.iloc[i]['message']\n",
+    "        m_new = \"\"\n",
+    "        for c in str(m):\n",
+    "            if ord(c) in emoji_code_set:\n",
+    "                emoji_list.append(c)\n",
+    "            elif ord(c) not in emoji_blacklist:\n",
+    "                m_new += c\n",
+    "        # if single label: only use last found emoji\n",
+    "        messages.loc[i,'emojis'] = set(emoji_list) if (not single_label.value) or len(emoji_list)==0 else set(emoji_list[-1])\n",
+    "        #remove emiójis from message\n",
+    "        messages.loc[i,'message'] = m_new\n",
+    "    \n",
+    "    emoji_messages = messages[[True if len(e) > 0 else False for e in messages['emojis']]]\n",
+    "    emoji_messages = emoji_messages[emoji_messages['message'] != \"\"]\n",
+    "    \n",
+    "    mp(\"**Done**\")\n",
+    "    \n",
+    "    display(emoji_messages)\n",
+    "\n",
+    "def train(b):\n",
+    "    global messages\n",
+    "    global emoji_messages\n",
+    "    global vectorizer\n",
+    "    global clf_a\n",
+    "    global mlb\n",
+    "    with out_train:\n",
+    "        clear_output()\n",
+    "        # train part:\n",
+    "        import numpy as np\n",
+    "        import itertools\n",
+    "        import sklearn.utils as sku\n",
+    "        from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n",
+    "        from sklearn.model_selection import train_test_split\n",
+    "        from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "\n",
+    "        mlb = MultiLabelBinarizer() if not single_label.value else None\n",
+    "        \n",
+    "        if not mlb:\n",
+    "            l = [list(e)[-1] for e in emoji_messages['emojis']]\n",
+    "        \n",
+    "        labels=mlb.fit_transform(emoji_messages['emojis']) if mlb else l\n",
+    "        \n",
+    "        if mlb:\n",
+    "            display(Markdown(\"**emojis contained in Dataset:**\\n >\" + \"\".join(mlb.classes_ )))\n",
+    "        else:\n",
+    "            display(Markdown(\"**emojis contained in Dataset:**\\n >\" + \"\".join(set(l))))\n",
+    "\n",
+    "        X1, Xt1, y1, yt1 = train_test_split(emoji_messages['message'], labels, test_size=0.1, random_state=4222)\n",
+    "\n",
+    "        vectorizer = TfidfVectorizer(stop_words='english')\n",
+    "        vec_train = vectorizer.fit_transform(X1)\n",
+    "        vec_test = vectorizer.transform(Xt1)\n",
+    "\n",
+    "        mp(\"**train classifier. This can take a very long time… Grab a coffe! 😀**\")\n",
+    "\n",
+    "        from sklearn.ensemble import RandomForestClassifier as RFC\n",
+    "        from sklearn.neural_network import MLPClassifier as MLP\n",
+    "        #from sklearn.naive_bayes import MultinomialNB as MNB\n",
+    "        from sklearn.tree import DecisionTreeClassifier as DTC\n",
+    "        from sklearn.multiclass import OneVsRestClassifier as OVRC\n",
+    "        clf_a = None\n",
+    "        if (d.value == \"DecisionTree\"):\n",
+    "            clf_a = DTC()\n",
+    "        elif d.value == \"MLP\":\n",
+    "            clf_a = MLP(hidden_layer_sizes=(64,))\n",
+    "        elif d.value == \"RandomForest\":\n",
+    "            RFC(criterion='entropy', random_state=4222)\n",
+    "\n",
+    "        if ova.value:\n",
+    "            clf_a=OVRC(clf_a)\n",
+    "\n",
+    "        display(clf_a)\n",
+    "        clf_a.fit(vec_train, y1)\n",
+    "\n",
+    "        mp(\"**training done**\")\n",
+    "\n",
+    "        pred = clf_a.predict(vectorizer.transform(Xt1))\n",
+    "\n",
+    "        testlist = pd.DataFrame({'message': Xt1, 'pred': mlb.inverse_transform(pred) if mlb else pred, 'teacher': mlb.inverse_transform(yt1) if mlb else yt1})\n",
+    "        testlist.to_csv('export.csv')\n",
+    "        display(testlist)\n",
+    "    \n",
+    "def build_db(b):\n",
+    "    with out_build:\n",
+    "        clear_output()\n",
+    "        create_emoji_set()\n",
+    "        read_message_and_build_db(t_build.value)\n",
+    "b_build.on_click(build_db)\n",
+    "b_train.on_click(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.externals import joblib\n",
+    "def write_to_file(b):\n",
+    "    global vectorizer\n",
+    "    global clf_a\n",
+    "    global mlb\n",
+    "    \n",
+    "    with out_save:\n",
+    "        clear_output()\n",
+    "        mp(\"**write to file...**\")\n",
+    "        joblib.dump(clf_a, t_save_c.value)\n",
+    "        if mlb:\n",
+    "            joblib.dump(mlb, t_save_m.value) \n",
+    "        joblib.dump(vectorizer, t_save_v.value)\n",
+    "        mp(\"**done**\")\n",
+    "b_save.on_click(write_to_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_from_file(b):\n",
+    "    global vectorizer\n",
+    "    global clf_a\n",
+    "    global mlb\n",
+    "    \n",
+    "    with out_read:\n",
+    "        clear_output()\n",
+    "        mp(\"**read from file…**\")\n",
+    "        clf_a = joblib.load(t_read_c.value)\n",
+    "        if t_read_m.value != \"\":\n",
+    "            mlb = joblib.load(t_read_m.value)\n",
+    "        else:\n",
+    "            mlb = None\n",
+    "        vectorizer = joblib.load(t_read_v.value)\n",
+    "        mp(\"**done**\")\n",
+    "b_read.on_click(read_from_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(b):\n",
+    "    with out_test:\n",
+    "        clear_output()\n",
+    "        v = mlb.inverse_transform(clf_a.predict(vectorizer.transform([t_test.value])))[0] if mlb else clf_a.predict(vectorizer.transform([t_test.value]))[0]\n",
+    "        mp(\"**prediction:**\\n# \" + (\"\".join(v) if len(v)>0 else \" \"))\n",
+    "        if b_prop.value:\n",
+    "            pred = clf_a.predict_proba(vectorizer.transform([t_test.value]))\n",
+    "            print(mlb.inverse_transform(pred))\n",
+    "\n",
+    "b_test.on_click(predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Project/Tools/emoji-data.txt b/Project/Tools/emoji-data.txt
new file mode 100644
index 0000000..6e66455
--- /dev/null
+++ b/Project/Tools/emoji-data.txt
@@ -0,0 +1,714 @@
+# emoji-data.txt
+# Date: 2018-02-07, 07:55:18 GMT
+# © 2018 Unicode®, Inc.
+# Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the U.S. and other countries.
+# For terms of use, see http://www.unicode.org/terms_of_use.html
+#
+# Emoji Data for UTS #51
+# Version: 11.0
+#
+# For documentation and usage, see http://www.unicode.org/reports/tr51
+#
+# Format: 
+# <codepoint(s)> ; <property> # <comments> 
+# Note: there is no guarantee as to the structure of whitespace or comments
+#
+# Characters and sequences are listed in code point order. Users should be shown a more natural order.
+# See the CLDR collation order for Emoji.
+
+
+# ================================================
+
+# All omitted code points have Emoji=No 
+# @missing: 0000..10FFFF  ; Emoji ; No
+
+0023          ; Emoji                #  1.1  [1] (#️)       number sign
+002A          ; Emoji                #  1.1  [1] (*️)       asterisk
+0030..0039    ; Emoji                #  1.1 [10] (0️..9️)    digit zero..digit nine
+00A9          ; Emoji                #  1.1  [1] (©️)       copyright
+00AE          ; Emoji                #  1.1  [1] (®️)       registered
+203C          ; Emoji                #  1.1  [1] (‼️)       double exclamation mark
+2049          ; Emoji                #  3.0  [1] (⁉️)       exclamation question mark
+2122          ; Emoji                #  1.1  [1] (™️)       trade mark
+2139          ; Emoji                #  3.0  [1] (ℹ️)       information
+2194..2199    ; Emoji                #  1.1  [6] (↔️..↙️)    left-right arrow..down-left arrow
+21A9..21AA    ; Emoji                #  1.1  [2] (↩️..↪️)    right arrow curving left..left arrow curving right
+231A..231B    ; Emoji                #  1.1  [2] (⌚..⌛)    watch..hourglass done
+2328          ; Emoji                #  1.1  [1] (⌨️)       keyboard
+23CF          ; Emoji                #  4.0  [1] (⏏️)       eject button
+23E9..23F3    ; Emoji                #  6.0 [11] (⏩..⏳)    fast-forward button..hourglass not done
+23F8..23FA    ; Emoji                #  7.0  [3] (⏸️..⏺️)    pause button..record button
+24C2          ; Emoji                #  1.1  [1] (Ⓜ️)       circled M
+25AA..25AB    ; Emoji                #  1.1  [2] (▪️..▫️)    black small square..white small square
+25B6          ; Emoji                #  1.1  [1] (▶️)       play button
+25C0          ; Emoji                #  1.1  [1] (◀️)       reverse button
+25FB..25FE    ; Emoji                #  3.2  [4] (◻️..◾)    white medium square..black medium-small square
+2600..2604    ; Emoji                #  1.1  [5] (☀️..☄️)    sun..comet
+260E          ; Emoji                #  1.1  [1] (☎️)       telephone
+2611          ; Emoji                #  1.1  [1] (☑️)       ballot box with check
+2614..2615    ; Emoji                #  4.0  [2] (☔..☕)    umbrella with rain drops..hot beverage
+2618          ; Emoji                #  4.1  [1] (☘️)       shamrock
+261D          ; Emoji                #  1.1  [1] (☝️)       index pointing up
+2620          ; Emoji                #  1.1  [1] (☠️)       skull and crossbones
+2622..2623    ; Emoji                #  1.1  [2] (☢️..☣️)    radioactive..biohazard
+2626          ; Emoji                #  1.1  [1] (☦️)       orthodox cross
+262A          ; Emoji                #  1.1  [1] (☪️)       star and crescent
+262E..262F    ; Emoji                #  1.1  [2] (☮️..☯️)    peace symbol..yin yang
+2638..263A    ; Emoji                #  1.1  [3] (☸️..☺️)    wheel of dharma..smiling face
+2640          ; Emoji                #  1.1  [1] (♀️)       female sign
+2642          ; Emoji                #  1.1  [1] (♂️)       male sign
+2648..2653    ; Emoji                #  1.1 [12] (♈..♓)    Aries..Pisces
+265F..2660    ; Emoji                #  1.1  [2] (♟️..♠️)    chess pawn..spade suit
+2663          ; Emoji                #  1.1  [1] (♣️)       club suit
+2665..2666    ; Emoji                #  1.1  [2] (♥️..♦️)    heart suit..diamond suit
+2668          ; Emoji                #  1.1  [1] (♨️)       hot springs
+267B          ; Emoji                #  3.2  [1] (♻️)       recycling symbol
+267E..267F    ; Emoji                #  4.1  [2] (♾️..♿)    infinity..wheelchair symbol
+2692..2697    ; Emoji                #  4.1  [6] (⚒️..⚗️)    hammer and pick..alembic
+2699          ; Emoji                #  4.1  [1] (⚙️)       gear
+269B..269C    ; Emoji                #  4.1  [2] (⚛️..⚜️)    atom symbol..fleur-de-lis
+26A0..26A1    ; Emoji                #  4.0  [2] (⚠️..⚡)    warning..high voltage
+26AA..26AB    ; Emoji                #  4.1  [2] (⚪..⚫)    white circle..black circle
+26B0..26B1    ; Emoji                #  4.1  [2] (⚰️..⚱️)    coffin..funeral urn
+26BD..26BE    ; Emoji                #  5.2  [2] (⚽..⚾)    soccer ball..baseball
+26C4..26C5    ; Emoji                #  5.2  [2] (⛄..⛅)    snowman without snow..sun behind cloud
+26C8          ; Emoji                #  5.2  [1] (⛈️)       cloud with lightning and rain
+26CE          ; Emoji                #  6.0  [1] (⛎)       Ophiuchus
+26CF          ; Emoji                #  5.2  [1] (⛏️)       pick
+26D1          ; Emoji                #  5.2  [1] (⛑️)       rescue worker’s helmet
+26D3..26D4    ; Emoji                #  5.2  [2] (⛓️..⛔)    chains..no entry
+26E9..26EA    ; Emoji                #  5.2  [2] (⛩️..⛪)    shinto shrine..church
+26F0..26F5    ; Emoji                #  5.2  [6] (⛰️..⛵)    mountain..sailboat
+26F7..26FA    ; Emoji                #  5.2  [4] (⛷️..⛺)    skier..tent
+26FD          ; Emoji                #  5.2  [1] (⛽)       fuel pump
+2702          ; Emoji                #  1.1  [1] (✂️)       scissors
+2705          ; Emoji                #  6.0  [1] (✅)       white heavy check mark
+2708..2709    ; Emoji                #  1.1  [2] (✈️..✉️)    airplane..envelope
+270A..270B    ; Emoji                #  6.0  [2] (✊..✋)    raised fist..raised hand
+270C..270D    ; Emoji                #  1.1  [2] (✌️..✍️)    victory hand..writing hand
+270F          ; Emoji                #  1.1  [1] (✏️)       pencil
+2712          ; Emoji                #  1.1  [1] (✒️)       black nib
+2714          ; Emoji                #  1.1  [1] (✔️)       heavy check mark
+2716          ; Emoji                #  1.1  [1] (✖️)       heavy multiplication x
+271D          ; Emoji                #  1.1  [1] (✝️)       latin cross
+2721          ; Emoji                #  1.1  [1] (✡️)       star of David
+2728          ; Emoji                #  6.0  [1] (✨)       sparkles
+2733..2734    ; Emoji                #  1.1  [2] (✳️..✴️)    eight-spoked asterisk..eight-pointed star
+2744          ; Emoji                #  1.1  [1] (❄️)       snowflake
+2747          ; Emoji                #  1.1  [1] (❇️)       sparkle
+274C          ; Emoji                #  6.0  [1] (❌)       cross mark
+274E          ; Emoji                #  6.0  [1] (❎)       cross mark button
+2753..2755    ; Emoji                #  6.0  [3] (❓..❕)    question mark..white exclamation mark
+2757          ; Emoji                #  5.2  [1] (❗)       exclamation mark
+2763..2764    ; Emoji                #  1.1  [2] (❣️..❤️)    heavy heart exclamation..red heart
+2795..2797    ; Emoji                #  6.0  [3] (➕..➗)    heavy plus sign..heavy division sign
+27A1          ; Emoji                #  1.1  [1] (➡️)       right arrow
+27B0          ; Emoji                #  6.0  [1] (➰)       curly loop
+27BF          ; Emoji                #  6.0  [1] (➿)       double curly loop
+2934..2935    ; Emoji                #  3.2  [2] (⤴️..⤵️)    right arrow curving up..right arrow curving down
+2B05..2B07    ; Emoji                #  4.0  [3] (⬅️..⬇️)    left arrow..down arrow
+2B1B..2B1C    ; Emoji                #  5.1  [2] (⬛..⬜)    black large square..white large square
+2B50          ; Emoji                #  5.1  [1] (⭐)       star
+2B55          ; Emoji                #  5.2  [1] (⭕)       heavy large circle
+3030          ; Emoji                #  1.1  [1] (〰️)       wavy dash
+303D          ; Emoji                #  3.2  [1] (〽️)       part alternation mark
+3297          ; Emoji                #  1.1  [1] (㊗️)       Japanese “congratulations” button
+3299          ; Emoji                #  1.1  [1] (㊙️)       Japanese “secret” button
+1F004         ; Emoji                #  5.1  [1] (🀄)       mahjong red dragon
+1F0CF         ; Emoji                #  6.0  [1] (🃏)       joker
+1F170..1F171  ; Emoji                #  6.0  [2] (🅰️..🅱️)    A button (blood type)..B button (blood type)
+1F17E         ; Emoji                #  6.0  [1] (🅾️)       O button (blood type)
+1F17F         ; Emoji                #  5.2  [1] (🅿️)       P button
+1F18E         ; Emoji                #  6.0  [1] (🆎)       AB button (blood type)
+1F191..1F19A  ; Emoji                #  6.0 [10] (🆑..🆚)    CL button..VS button
+1F1E6..1F1FF  ; Emoji                #  6.0 [26] (🇦..🇿)    regional indicator symbol letter a..regional indicator symbol letter z
+1F201..1F202  ; Emoji                #  6.0  [2] (🈁..🈂️)    Japanese “here” button..Japanese “service charge” button
+1F21A         ; Emoji                #  5.2  [1] (🈚)       Japanese “free of charge” button
+1F22F         ; Emoji                #  5.2  [1] (🈯)       Japanese “reserved” button
+1F232..1F23A  ; Emoji                #  6.0  [9] (🈲..🈺)    Japanese “prohibited” button..Japanese “open for business” button
+1F250..1F251  ; Emoji                #  6.0  [2] (🉐..🉑)    Japanese “bargain” button..Japanese “acceptable” button
+1F300..1F320  ; Emoji                #  6.0 [33] (🌀..🌠)    cyclone..shooting star
+1F321         ; Emoji                #  7.0  [1] (🌡️)       thermometer
+1F324..1F32C  ; Emoji                #  7.0  [9] (🌤️..🌬️)    sun behind small cloud..wind face
+1F32D..1F32F  ; Emoji                #  8.0  [3] (🌭..🌯)    hot dog..burrito
+1F330..1F335  ; Emoji                #  6.0  [6] (🌰..🌵)    chestnut..cactus
+1F336         ; Emoji                #  7.0  [1] (🌶️)       hot pepper
+1F337..1F37C  ; Emoji                #  6.0 [70] (🌷..🍼)    tulip..baby bottle
+1F37D         ; Emoji                #  7.0  [1] (🍽️)       fork and knife with plate
+1F37E..1F37F  ; Emoji                #  8.0  [2] (🍾..🍿)    bottle with popping cork..popcorn
+1F380..1F393  ; Emoji                #  6.0 [20] (🎀..🎓)    ribbon..graduation cap
+1F396..1F397  ; Emoji                #  7.0  [2] (🎖️..🎗️)    military medal..reminder ribbon
+1F399..1F39B  ; Emoji                #  7.0  [3] (🎙️..🎛️)    studio microphone..control knobs
+1F39E..1F39F  ; Emoji                #  7.0  [2] (🎞️..🎟️)    film frames..admission tickets
+1F3A0..1F3C4  ; Emoji                #  6.0 [37] (🎠..🏄)    carousel horse..person surfing
+1F3C5         ; Emoji                #  7.0  [1] (🏅)       sports medal
+1F3C6..1F3CA  ; Emoji                #  6.0  [5] (🏆..🏊)    trophy..person swimming
+1F3CB..1F3CE  ; Emoji                #  7.0  [4] (🏋️..🏎️)    person lifting weights..racing car
+1F3CF..1F3D3  ; Emoji                #  8.0  [5] (🏏..🏓)    cricket game..ping pong
+1F3D4..1F3DF  ; Emoji                #  7.0 [12] (🏔️..🏟️)    snow-capped mountain..stadium
+1F3E0..1F3F0  ; Emoji                #  6.0 [17] (🏠..🏰)    house..castle
+1F3F3..1F3F5  ; Emoji                #  7.0  [3] (🏳️..🏵️)    white flag..rosette
+1F3F7         ; Emoji                #  7.0  [1] (🏷️)       label
+1F3F8..1F3FF  ; Emoji                #  8.0  [8] (🏸..🏿)    badminton..dark skin tone
+1F400..1F43E  ; Emoji                #  6.0 [63] (🐀..🐾)    rat..paw prints
+1F43F         ; Emoji                #  7.0  [1] (🐿️)       chipmunk
+1F440         ; Emoji                #  6.0  [1] (👀)       eyes
+1F441         ; Emoji                #  7.0  [1] (👁️)       eye
+1F442..1F4F7  ; Emoji                #  6.0[182] (👂..📷)    ear..camera
+1F4F8         ; Emoji                #  7.0  [1] (📸)       camera with flash
+1F4F9..1F4FC  ; Emoji                #  6.0  [4] (📹..📼)    video camera..videocassette
+1F4FD         ; Emoji                #  7.0  [1] (📽️)       film projector
+1F4FF         ; Emoji                #  8.0  [1] (📿)       prayer beads
+1F500..1F53D  ; Emoji                #  6.0 [62] (🔀..🔽)    shuffle tracks button..downwards button
+1F549..1F54A  ; Emoji                #  7.0  [2] (🕉️..🕊️)    om..dove
+1F54B..1F54E  ; Emoji                #  8.0  [4] (🕋..🕎)    kaaba..menorah
+1F550..1F567  ; Emoji                #  6.0 [24] (🕐..🕧)    one o’clock..twelve-thirty
+1F56F..1F570  ; Emoji                #  7.0  [2] (🕯️..🕰️)    candle..mantelpiece clock
+1F573..1F579  ; Emoji                #  7.0  [7] (🕳️..🕹️)    hole..joystick
+1F57A         ; Emoji                #  9.0  [1] (🕺)       man dancing
+1F587         ; Emoji                #  7.0  [1] (🖇️)       linked paperclips
+1F58A..1F58D  ; Emoji                #  7.0  [4] (🖊️..🖍️)    pen..crayon
+1F590         ; Emoji                #  7.0  [1] (🖐️)       hand with fingers splayed
+1F595..1F596  ; Emoji                #  7.0  [2] (🖕..🖖)    middle finger..vulcan salute
+1F5A4         ; Emoji                #  9.0  [1] (🖤)       black heart
+1F5A5         ; Emoji                #  7.0  [1] (🖥️)       desktop computer
+1F5A8         ; Emoji                #  7.0  [1] (🖨️)       printer
+1F5B1..1F5B2  ; Emoji                #  7.0  [2] (🖱️..🖲️)    computer mouse..trackball
+1F5BC         ; Emoji                #  7.0  [1] (🖼️)       framed picture
+1F5C2..1F5C4  ; Emoji                #  7.0  [3] (🗂️..🗄️)    card index dividers..file cabinet
+1F5D1..1F5D3  ; Emoji                #  7.0  [3] (🗑️..🗓️)    wastebasket..spiral calendar
+1F5DC..1F5DE  ; Emoji                #  7.0  [3] (🗜️..🗞️)    clamp..rolled-up newspaper
+1F5E1         ; Emoji                #  7.0  [1] (🗡️)       dagger
+1F5E3         ; Emoji                #  7.0  [1] (🗣️)       speaking head
+1F5E8         ; Emoji                #  7.0  [1] (🗨️)       left speech bubble
+1F5EF         ; Emoji                #  7.0  [1] (🗯️)       right anger bubble
+1F5F3         ; Emoji                #  7.0  [1] (🗳️)       ballot box with ballot
+1F5FA         ; Emoji                #  7.0  [1] (🗺️)       world map
+1F5FB..1F5FF  ; Emoji                #  6.0  [5] (🗻..🗿)    mount fuji..moai
+1F600         ; Emoji                #  6.1  [1] (😀)       grinning face
+1F601..1F610  ; Emoji                #  6.0 [16] (😁..😐)    beaming face with smiling eyes..neutral face
+1F611         ; Emoji                #  6.1  [1] (😑)       expressionless face
+1F612..1F614  ; Emoji                #  6.0  [3] (😒..😔)    unamused face..pensive face
+1F615         ; Emoji                #  6.1  [1] (😕)       confused face
+1F616         ; Emoji                #  6.0  [1] (😖)       confounded face
+1F617         ; Emoji                #  6.1  [1] (😗)       kissing face
+1F618         ; Emoji                #  6.0  [1] (😘)       face blowing a kiss
+1F619         ; Emoji                #  6.1  [1] (😙)       kissing face with smiling eyes
+1F61A         ; Emoji                #  6.0  [1] (😚)       kissing face with closed eyes
+1F61B         ; Emoji                #  6.1  [1] (😛)       face with tongue
+1F61C..1F61E  ; Emoji                #  6.0  [3] (😜..😞)    winking face with tongue..disappointed face
+1F61F         ; Emoji                #  6.1  [1] (😟)       worried face
+1F620..1F625  ; Emoji                #  6.0  [6] (😠..😥)    angry face..sad but relieved face
+1F626..1F627  ; Emoji                #  6.1  [2] (😦..😧)    frowning face with open mouth..anguished face
+1F628..1F62B  ; Emoji                #  6.0  [4] (😨..😫)    fearful face..tired face
+1F62C         ; Emoji                #  6.1  [1] (😬)       grimacing face
+1F62D         ; Emoji                #  6.0  [1] (😭)       loudly crying face
+1F62E..1F62F  ; Emoji                #  6.1  [2] (😮..😯)    face with open mouth..hushed face
+1F630..1F633  ; Emoji                #  6.0  [4] (😰..😳)    anxious face with sweat..flushed face
+1F634         ; Emoji                #  6.1  [1] (😴)       sleeping face
+1F635..1F640  ; Emoji                #  6.0 [12] (😵..🙀)    dizzy face..weary cat face
+1F641..1F642  ; Emoji                #  7.0  [2] (🙁..🙂)    slightly frowning face..slightly smiling face
+1F643..1F644  ; Emoji                #  8.0  [2] (🙃..🙄)    upside-down face..face with rolling eyes
+1F645..1F64F  ; Emoji                #  6.0 [11] (🙅..🙏)    person gesturing NO..folded hands
+1F680..1F6C5  ; Emoji                #  6.0 [70] (🚀..🛅)    rocket..left luggage
+1F6CB..1F6CF  ; Emoji                #  7.0  [5] (🛋️..🛏️)    couch and lamp..bed
+1F6D0         ; Emoji                #  8.0  [1] (🛐)       place of worship
+1F6D1..1F6D2  ; Emoji                #  9.0  [2] (🛑..🛒)    stop sign..shopping cart
+1F6E0..1F6E5  ; Emoji                #  7.0  [6] (🛠️..🛥️)    hammer and wrench..motor boat
+1F6E9         ; Emoji                #  7.0  [1] (🛩️)       small airplane
+1F6EB..1F6EC  ; Emoji                #  7.0  [2] (🛫..🛬)    airplane departure..airplane arrival
+1F6F0         ; Emoji                #  7.0  [1] (🛰️)       satellite
+1F6F3         ; Emoji                #  7.0  [1] (🛳️)       passenger ship
+1F6F4..1F6F6  ; Emoji                #  9.0  [3] (🛴..🛶)    kick scooter..canoe
+1F6F7..1F6F8  ; Emoji                # 10.0  [2] (🛷..🛸)    sled..flying saucer
+1F6F9         ; Emoji                # 11.0  [1] (🛹)       skateboard
+1F910..1F918  ; Emoji                #  8.0  [9] (🤐..🤘)    zipper-mouth face..sign of the horns
+1F919..1F91E  ; Emoji                #  9.0  [6] (🤙..🤞)    call me hand..crossed fingers
+1F91F         ; Emoji                # 10.0  [1] (🤟)       love-you gesture
+1F920..1F927  ; Emoji                #  9.0  [8] (🤠..🤧)    cowboy hat face..sneezing face
+1F928..1F92F  ; Emoji                # 10.0  [8] (🤨..🤯)    face with raised eyebrow..exploding head
+1F930         ; Emoji                #  9.0  [1] (🤰)       pregnant woman
+1F931..1F932  ; Emoji                # 10.0  [2] (🤱..🤲)    breast-feeding..palms up together
+1F933..1F93A  ; Emoji                #  9.0  [8] (🤳..🤺)    selfie..person fencing
+1F93C..1F93E  ; Emoji                #  9.0  [3] (🤼..🤾)    people wrestling..person playing handball
+1F940..1F945  ; Emoji                #  9.0  [6] (🥀..🥅)    wilted flower..goal net
+1F947..1F94B  ; Emoji                #  9.0  [5] (🥇..🥋)    1st place medal..martial arts uniform
+1F94C         ; Emoji                # 10.0  [1] (🥌)       curling stone
+1F94D..1F94F  ; Emoji                # 11.0  [3] (🥍..🥏)    lacrosse..flying disc
+1F950..1F95E  ; Emoji                #  9.0 [15] (🥐..🥞)    croissant..pancakes
+1F95F..1F96B  ; Emoji                # 10.0 [13] (🥟..🥫)    dumpling..canned food
+1F96C..1F970  ; Emoji                # 11.0  [5] (🥬..🥰)    leafy green..smiling face with 3 hearts
+1F973..1F976  ; Emoji                # 11.0  [4] (🥳..🥶)    partying face..cold face
+1F97A         ; Emoji                # 11.0  [1] (🥺)       pleading face
+1F97C..1F97F  ; Emoji                # 11.0  [4] (🥼..🥿)    lab coat..woman’s flat shoe
+1F980..1F984  ; Emoji                #  8.0  [5] (🦀..🦄)    crab..unicorn face
+1F985..1F991  ; Emoji                #  9.0 [13] (🦅..🦑)    eagle..squid
+1F992..1F997  ; Emoji                # 10.0  [6] (🦒..🦗)    giraffe..cricket
+1F998..1F9A2  ; Emoji                # 11.0 [11] (🦘..🦢)    kangaroo..swan
+1F9B0..1F9B9  ; Emoji                # 11.0 [10] (🦰..🦹)    red-haired..supervillain
+1F9C0         ; Emoji                #  8.0  [1] (🧀)       cheese wedge
+1F9C1..1F9C2  ; Emoji                # 11.0  [2] (🧁..🧂)    cupcake..salt
+1F9D0..1F9E6  ; Emoji                # 10.0 [23] (🧐..🧦)    face with monocle..socks
+1F9E7..1F9FF  ; Emoji                # 11.0 [25] (🧧..🧿)    red envelope..nazar amulet
+
+# Total elements: 1250
+
+# ================================================
+
+# All omitted code points have Emoji_Presentation=No 
+# @missing: 0000..10FFFF  ; Emoji_Presentation ; No
+
+231A..231B    ; Emoji_Presentation   #  1.1  [2] (⌚..⌛)    watch..hourglass done
+23E9..23EC    ; Emoji_Presentation   #  6.0  [4] (⏩..⏬)    fast-forward button..fast down button
+23F0          ; Emoji_Presentation   #  6.0  [1] (⏰)       alarm clock
+23F3          ; Emoji_Presentation   #  6.0  [1] (⏳)       hourglass not done
+25FD..25FE    ; Emoji_Presentation   #  3.2  [2] (◽..◾)    white medium-small square..black medium-small square
+2614..2615    ; Emoji_Presentation   #  4.0  [2] (☔..☕)    umbrella with rain drops..hot beverage
+2648..2653    ; Emoji_Presentation   #  1.1 [12] (♈..♓)    Aries..Pisces
+267F          ; Emoji_Presentation   #  4.1  [1] (♿)       wheelchair symbol
+2693          ; Emoji_Presentation   #  4.1  [1] (⚓)       anchor
+26A1          ; Emoji_Presentation   #  4.0  [1] (⚡)       high voltage
+26AA..26AB    ; Emoji_Presentation   #  4.1  [2] (⚪..⚫)    white circle..black circle
+26BD..26BE    ; Emoji_Presentation   #  5.2  [2] (⚽..⚾)    soccer ball..baseball
+26C4..26C5    ; Emoji_Presentation   #  5.2  [2] (⛄..⛅)    snowman without snow..sun behind cloud
+26CE          ; Emoji_Presentation   #  6.0  [1] (⛎)       Ophiuchus
+26D4          ; Emoji_Presentation   #  5.2  [1] (⛔)       no entry
+26EA          ; Emoji_Presentation   #  5.2  [1] (⛪)       church
+26F2..26F3    ; Emoji_Presentation   #  5.2  [2] (⛲..⛳)    fountain..flag in hole
+26F5          ; Emoji_Presentation   #  5.2  [1] (⛵)       sailboat
+26FA          ; Emoji_Presentation   #  5.2  [1] (⛺)       tent
+26FD          ; Emoji_Presentation   #  5.2  [1] (⛽)       fuel pump
+2705          ; Emoji_Presentation   #  6.0  [1] (✅)       white heavy check mark
+270A..270B    ; Emoji_Presentation   #  6.0  [2] (✊..✋)    raised fist..raised hand
+2728          ; Emoji_Presentation   #  6.0  [1] (✨)       sparkles
+274C          ; Emoji_Presentation   #  6.0  [1] (❌)       cross mark
+274E          ; Emoji_Presentation   #  6.0  [1] (❎)       cross mark button
+2753..2755    ; Emoji_Presentation   #  6.0  [3] (❓..❕)    question mark..white exclamation mark
+2757          ; Emoji_Presentation   #  5.2  [1] (❗)       exclamation mark
+2795..2797    ; Emoji_Presentation   #  6.0  [3] (➕..➗)    heavy plus sign..heavy division sign
+27B0          ; Emoji_Presentation   #  6.0  [1] (➰)       curly loop
+27BF          ; Emoji_Presentation   #  6.0  [1] (➿)       double curly loop
+2B1B..2B1C    ; Emoji_Presentation   #  5.1  [2] (⬛..⬜)    black large square..white large square
+2B50          ; Emoji_Presentation   #  5.1  [1] (⭐)       star
+2B55          ; Emoji_Presentation   #  5.2  [1] (⭕)       heavy large circle
+1F004         ; Emoji_Presentation   #  5.1  [1] (🀄)       mahjong red dragon
+1F0CF         ; Emoji_Presentation   #  6.0  [1] (🃏)       joker
+1F18E         ; Emoji_Presentation   #  6.0  [1] (🆎)       AB button (blood type)
+1F191..1F19A  ; Emoji_Presentation   #  6.0 [10] (🆑..🆚)    CL button..VS button
+1F1E6..1F1FF  ; Emoji_Presentation   #  6.0 [26] (🇦..🇿)    regional indicator symbol letter a..regional indicator symbol letter z
+1F201         ; Emoji_Presentation   #  6.0  [1] (🈁)       Japanese “here” button
+1F21A         ; Emoji_Presentation   #  5.2  [1] (🈚)       Japanese “free of charge” button
+1F22F         ; Emoji_Presentation   #  5.2  [1] (🈯)       Japanese “reserved” button
+1F232..1F236  ; Emoji_Presentation   #  6.0  [5] (🈲..🈶)    Japanese “prohibited” button..Japanese “not free of charge” button
+1F238..1F23A  ; Emoji_Presentation   #  6.0  [3] (🈸..🈺)    Japanese “application” button..Japanese “open for business” button
+1F250..1F251  ; Emoji_Presentation   #  6.0  [2] (🉐..🉑)    Japanese “bargain” button..Japanese “acceptable” button
+1F300..1F320  ; Emoji_Presentation   #  6.0 [33] (🌀..🌠)    cyclone..shooting star
+1F32D..1F32F  ; Emoji_Presentation   #  8.0  [3] (🌭..🌯)    hot dog..burrito
+1F330..1F335  ; Emoji_Presentation   #  6.0  [6] (🌰..🌵)    chestnut..cactus
+1F337..1F37C  ; Emoji_Presentation   #  6.0 [70] (🌷..🍼)    tulip..baby bottle
+1F37E..1F37F  ; Emoji_Presentation   #  8.0  [2] (🍾..🍿)    bottle with popping cork..popcorn
+1F380..1F393  ; Emoji_Presentation   #  6.0 [20] (🎀..🎓)    ribbon..graduation cap
+1F3A0..1F3C4  ; Emoji_Presentation   #  6.0 [37] (🎠..🏄)    carousel horse..person surfing
+1F3C5         ; Emoji_Presentation   #  7.0  [1] (🏅)       sports medal
+1F3C6..1F3CA  ; Emoji_Presentation   #  6.0  [5] (🏆..🏊)    trophy..person swimming
+1F3CF..1F3D3  ; Emoji_Presentation   #  8.0  [5] (🏏..🏓)    cricket game..ping pong
+1F3E0..1F3F0  ; Emoji_Presentation   #  6.0 [17] (🏠..🏰)    house..castle
+1F3F4         ; Emoji_Presentation   #  7.0  [1] (🏴)       black flag
+1F3F8..1F3FF  ; Emoji_Presentation   #  8.0  [8] (🏸..🏿)    badminton..dark skin tone
+1F400..1F43E  ; Emoji_Presentation   #  6.0 [63] (🐀..🐾)    rat..paw prints
+1F440         ; Emoji_Presentation   #  6.0  [1] (👀)       eyes
+1F442..1F4F7  ; Emoji_Presentation   #  6.0[182] (👂..📷)    ear..camera
+1F4F8         ; Emoji_Presentation   #  7.0  [1] (📸)       camera with flash
+1F4F9..1F4FC  ; Emoji_Presentation   #  6.0  [4] (📹..📼)    video camera..videocassette
+1F4FF         ; Emoji_Presentation   #  8.0  [1] (📿)       prayer beads
+1F500..1F53D  ; Emoji_Presentation   #  6.0 [62] (🔀..🔽)    shuffle tracks button..downwards button
+1F54B..1F54E  ; Emoji_Presentation   #  8.0  [4] (🕋..🕎)    kaaba..menorah
+1F550..1F567  ; Emoji_Presentation   #  6.0 [24] (🕐..🕧)    one o’clock..twelve-thirty
+1F57A         ; Emoji_Presentation   #  9.0  [1] (🕺)       man dancing
+1F595..1F596  ; Emoji_Presentation   #  7.0  [2] (🖕..🖖)    middle finger..vulcan salute
+1F5A4         ; Emoji_Presentation   #  9.0  [1] (🖤)       black heart
+1F5FB..1F5FF  ; Emoji_Presentation   #  6.0  [5] (🗻..🗿)    mount fuji..moai
+1F600         ; Emoji_Presentation   #  6.1  [1] (😀)       grinning face
+1F601..1F610  ; Emoji_Presentation   #  6.0 [16] (😁..😐)    beaming face with smiling eyes..neutral face
+1F611         ; Emoji_Presentation   #  6.1  [1] (😑)       expressionless face
+1F612..1F614  ; Emoji_Presentation   #  6.0  [3] (😒..😔)    unamused face..pensive face
+1F615         ; Emoji_Presentation   #  6.1  [1] (😕)       confused face
+1F616         ; Emoji_Presentation   #  6.0  [1] (😖)       confounded face
+1F617         ; Emoji_Presentation   #  6.1  [1] (😗)       kissing face
+1F618         ; Emoji_Presentation   #  6.0  [1] (😘)       face blowing a kiss
+1F619         ; Emoji_Presentation   #  6.1  [1] (😙)       kissing face with smiling eyes
+1F61A         ; Emoji_Presentation   #  6.0  [1] (😚)       kissing face with closed eyes
+1F61B         ; Emoji_Presentation   #  6.1  [1] (😛)       face with tongue
+1F61C..1F61E  ; Emoji_Presentation   #  6.0  [3] (😜..😞)    winking face with tongue..disappointed face
+1F61F         ; Emoji_Presentation   #  6.1  [1] (😟)       worried face
+1F620..1F625  ; Emoji_Presentation   #  6.0  [6] (😠..😥)    angry face..sad but relieved face
+1F626..1F627  ; Emoji_Presentation   #  6.1  [2] (😦..😧)    frowning face with open mouth..anguished face
+1F628..1F62B  ; Emoji_Presentation   #  6.0  [4] (😨..😫)    fearful face..tired face
+1F62C         ; Emoji_Presentation   #  6.1  [1] (😬)       grimacing face
+1F62D         ; Emoji_Presentation   #  6.0  [1] (😭)       loudly crying face
+1F62E..1F62F  ; Emoji_Presentation   #  6.1  [2] (😮..😯)    face with open mouth..hushed face
+1F630..1F633  ; Emoji_Presentation   #  6.0  [4] (😰..😳)    anxious face with sweat..flushed face
+1F634         ; Emoji_Presentation   #  6.1  [1] (😴)       sleeping face
+1F635..1F640  ; Emoji_Presentation   #  6.0 [12] (😵..🙀)    dizzy face..weary cat face
+1F641..1F642  ; Emoji_Presentation   #  7.0  [2] (🙁..🙂)    slightly frowning face..slightly smiling face
+1F643..1F644  ; Emoji_Presentation   #  8.0  [2] (🙃..🙄)    upside-down face..face with rolling eyes
+1F645..1F64F  ; Emoji_Presentation   #  6.0 [11] (🙅..🙏)    person gesturing NO..folded hands
+1F680..1F6C5  ; Emoji_Presentation   #  6.0 [70] (🚀..🛅)    rocket..left luggage
+1F6CC         ; Emoji_Presentation   #  7.0  [1] (🛌)       person in bed
+1F6D0         ; Emoji_Presentation   #  8.0  [1] (🛐)       place of worship
+1F6D1..1F6D2  ; Emoji_Presentation   #  9.0  [2] (🛑..🛒)    stop sign..shopping cart
+1F6EB..1F6EC  ; Emoji_Presentation   #  7.0  [2] (🛫..🛬)    airplane departure..airplane arrival
+1F6F4..1F6F6  ; Emoji_Presentation   #  9.0  [3] (🛴..🛶)    kick scooter..canoe
+1F6F7..1F6F8  ; Emoji_Presentation   # 10.0  [2] (🛷..🛸)    sled..flying saucer
+1F6F9         ; Emoji_Presentation   # 11.0  [1] (🛹)       skateboard
+1F910..1F918  ; Emoji_Presentation   #  8.0  [9] (🤐..🤘)    zipper-mouth face..sign of the horns
+1F919..1F91E  ; Emoji_Presentation   #  9.0  [6] (🤙..🤞)    call me hand..crossed fingers
+1F91F         ; Emoji_Presentation   # 10.0  [1] (🤟)       love-you gesture
+1F920..1F927  ; Emoji_Presentation   #  9.0  [8] (🤠..🤧)    cowboy hat face..sneezing face
+1F928..1F92F  ; Emoji_Presentation   # 10.0  [8] (🤨..🤯)    face with raised eyebrow..exploding head
+1F930         ; Emoji_Presentation   #  9.0  [1] (🤰)       pregnant woman
+1F931..1F932  ; Emoji_Presentation   # 10.0  [2] (🤱..🤲)    breast-feeding..palms up together
+1F933..1F93A  ; Emoji_Presentation   #  9.0  [8] (🤳..🤺)    selfie..person fencing
+1F93C..1F93E  ; Emoji_Presentation   #  9.0  [3] (🤼..🤾)    people wrestling..person playing handball
+1F940..1F945  ; Emoji_Presentation   #  9.0  [6] (🥀..🥅)    wilted flower..goal net
+1F947..1F94B  ; Emoji_Presentation   #  9.0  [5] (🥇..🥋)    1st place medal..martial arts uniform
+1F94C         ; Emoji_Presentation   # 10.0  [1] (🥌)       curling stone
+1F94D..1F94F  ; Emoji_Presentation   # 11.0  [3] (🥍..🥏)    lacrosse..flying disc
+1F950..1F95E  ; Emoji_Presentation   #  9.0 [15] (🥐..🥞)    croissant..pancakes
+1F95F..1F96B  ; Emoji_Presentation   # 10.0 [13] (🥟..🥫)    dumpling..canned food
+1F96C..1F970  ; Emoji_Presentation   # 11.0  [5] (🥬..🥰)    leafy green..smiling face with 3 hearts
+1F973..1F976  ; Emoji_Presentation   # 11.0  [4] (🥳..🥶)    partying face..cold face
+1F97A         ; Emoji_Presentation   # 11.0  [1] (🥺)       pleading face
+1F97C..1F97F  ; Emoji_Presentation   # 11.0  [4] (🥼..🥿)    lab coat..woman’s flat shoe
+1F980..1F984  ; Emoji_Presentation   #  8.0  [5] (🦀..🦄)    crab..unicorn face
+1F985..1F991  ; Emoji_Presentation   #  9.0 [13] (🦅..🦑)    eagle..squid
+1F992..1F997  ; Emoji_Presentation   # 10.0  [6] (🦒..🦗)    giraffe..cricket
+1F998..1F9A2  ; Emoji_Presentation   # 11.0 [11] (🦘..🦢)    kangaroo..swan
+1F9B0..1F9B9  ; Emoji_Presentation   # 11.0 [10] (🦰..🦹)    red-haired..supervillain
+1F9C0         ; Emoji_Presentation   #  8.0  [1] (🧀)       cheese wedge
+1F9C1..1F9C2  ; Emoji_Presentation   # 11.0  [2] (🧁..🧂)    cupcake..salt
+1F9D0..1F9E6  ; Emoji_Presentation   # 10.0 [23] (🧐..🧦)    face with monocle..socks
+1F9E7..1F9FF  ; Emoji_Presentation   # 11.0 [25] (🧧..🧿)    red envelope..nazar amulet
+
+# Total elements: 1032
+
+# ================================================
+
+# All omitted code points have Emoji_Modifier=No 
+# @missing: 0000..10FFFF  ; Emoji_Modifier ; No
+
+1F3FB..1F3FF  ; Emoji_Modifier       #  8.0  [5] (🏻..🏿)    light skin tone..dark skin tone
+
+# Total elements: 5
+
+# ================================================
+
+# All omitted code points have Emoji_Modifier_Base=No 
+# @missing: 0000..10FFFF  ; Emoji_Modifier_Base ; No
+
+261D          ; Emoji_Modifier_Base  #  1.1  [1] (☝️)       index pointing up
+26F9          ; Emoji_Modifier_Base  #  5.2  [1] (⛹️)       person bouncing ball
+270A..270B    ; Emoji_Modifier_Base  #  6.0  [2] (✊..✋)    raised fist..raised hand
+270C..270D    ; Emoji_Modifier_Base  #  1.1  [2] (✌️..✍️)    victory hand..writing hand
+1F385         ; Emoji_Modifier_Base  #  6.0  [1] (🎅)       Santa Claus
+1F3C2..1F3C4  ; Emoji_Modifier_Base  #  6.0  [3] (🏂..🏄)    snowboarder..person surfing
+1F3C7         ; Emoji_Modifier_Base  #  6.0  [1] (🏇)       horse racing
+1F3CA         ; Emoji_Modifier_Base  #  6.0  [1] (🏊)       person swimming
+1F3CB..1F3CC  ; Emoji_Modifier_Base  #  7.0  [2] (🏋️..🏌️)    person lifting weights..person golfing
+1F442..1F443  ; Emoji_Modifier_Base  #  6.0  [2] (👂..👃)    ear..nose
+1F446..1F450  ; Emoji_Modifier_Base  #  6.0 [11] (👆..👐)    backhand index pointing up..open hands
+1F466..1F469  ; Emoji_Modifier_Base  #  6.0  [4] (👦..👩)    boy..woman
+1F46E         ; Emoji_Modifier_Base  #  6.0  [1] (👮)       police officer
+1F470..1F478  ; Emoji_Modifier_Base  #  6.0  [9] (👰..👸)    bride with veil..princess
+1F47C         ; Emoji_Modifier_Base  #  6.0  [1] (👼)       baby angel
+1F481..1F483  ; Emoji_Modifier_Base  #  6.0  [3] (💁..💃)    person tipping hand..woman dancing
+1F485..1F487  ; Emoji_Modifier_Base  #  6.0  [3] (💅..💇)    nail polish..person getting haircut
+1F4AA         ; Emoji_Modifier_Base  #  6.0  [1] (💪)       flexed biceps
+1F574..1F575  ; Emoji_Modifier_Base  #  7.0  [2] (🕴️..🕵️)    man in suit levitating..detective
+1F57A         ; Emoji_Modifier_Base  #  9.0  [1] (🕺)       man dancing
+1F590         ; Emoji_Modifier_Base  #  7.0  [1] (🖐️)       hand with fingers splayed
+1F595..1F596  ; Emoji_Modifier_Base  #  7.0  [2] (🖕..🖖)    middle finger..vulcan salute
+1F645..1F647  ; Emoji_Modifier_Base  #  6.0  [3] (🙅..🙇)    person gesturing NO..person bowing
+1F64B..1F64F  ; Emoji_Modifier_Base  #  6.0  [5] (🙋..🙏)    person raising hand..folded hands
+1F6A3         ; Emoji_Modifier_Base  #  6.0  [1] (🚣)       person rowing boat
+1F6B4..1F6B6  ; Emoji_Modifier_Base  #  6.0  [3] (🚴..🚶)    person biking..person walking
+1F6C0         ; Emoji_Modifier_Base  #  6.0  [1] (🛀)       person taking bath
+1F6CC         ; Emoji_Modifier_Base  #  7.0  [1] (🛌)       person in bed
+1F918         ; Emoji_Modifier_Base  #  8.0  [1] (🤘)       sign of the horns
+1F919..1F91C  ; Emoji_Modifier_Base  #  9.0  [4] (🤙..🤜)    call me hand..right-facing fist
+1F91E         ; Emoji_Modifier_Base  #  9.0  [1] (🤞)       crossed fingers
+1F91F         ; Emoji_Modifier_Base  # 10.0  [1] (🤟)       love-you gesture
+1F926         ; Emoji_Modifier_Base  #  9.0  [1] (🤦)       person facepalming
+1F930         ; Emoji_Modifier_Base  #  9.0  [1] (🤰)       pregnant woman
+1F931..1F932  ; Emoji_Modifier_Base  # 10.0  [2] (🤱..🤲)    breast-feeding..palms up together
+1F933..1F939  ; Emoji_Modifier_Base  #  9.0  [7] (🤳..🤹)    selfie..person juggling
+1F93D..1F93E  ; Emoji_Modifier_Base  #  9.0  [2] (🤽..🤾)    person playing water polo..person playing handball
+1F9B5..1F9B6  ; Emoji_Modifier_Base  # 11.0  [2] (🦵..🦶)    leg..foot
+1F9B8..1F9B9  ; Emoji_Modifier_Base  # 11.0  [2] (🦸..🦹)    superhero..supervillain
+1F9D1..1F9DD  ; Emoji_Modifier_Base  # 10.0 [13] (🧑..🧝)    adult..elf
+
+# Total elements: 106
+
+# ================================================
+
+# All omitted code points have Emoji_Component=No 
+# @missing: 0000..10FFFF  ; Emoji_Component ; No
+
+0023          ; Emoji_Component      #  1.1  [1] (#️)       number sign
+002A          ; Emoji_Component      #  1.1  [1] (*️)       asterisk
+0030..0039    ; Emoji_Component      #  1.1 [10] (0️..9️)    digit zero..digit nine
+200D          ; Emoji_Component      #  1.1  [1] (‍)        zero width joiner
+20E3          ; Emoji_Component      #  3.0  [1] (⃣)       combining enclosing keycap
+FE0F          ; Emoji_Component      #  3.2  [1] ()        VARIATION SELECTOR-16
+1F1E6..1F1FF  ; Emoji_Component      #  6.0 [26] (🇦..🇿)    regional indicator symbol letter a..regional indicator symbol letter z
+1F3FB..1F3FF  ; Emoji_Component      #  8.0  [5] (🏻..🏿)    light skin tone..dark skin tone
+1F9B0..1F9B3  ; Emoji_Component      # 11.0  [4] (🦰..🦳)    red-haired..white-haired
+E0020..E007F  ; Emoji_Component      #  3.1 [96] (󠀠..󠁿)      tag space..cancel tag
+
+# Total elements: 146
+
+# ================================================
+
+# All omitted code points have Extended_Pictographic=No 
+# @missing: 0000..10FFFF  ; Extended_Pictographic ; No
+
+00A9          ; Extended_Pictographic#  1.1  [1] (©️)       copyright
+00AE          ; Extended_Pictographic#  1.1  [1] (®️)       registered
+203C          ; Extended_Pictographic#  1.1  [1] (‼️)       double exclamation mark
+2049          ; Extended_Pictographic#  3.0  [1] (⁉️)       exclamation question mark
+2122          ; Extended_Pictographic#  1.1  [1] (™️)       trade mark
+2139          ; Extended_Pictographic#  3.0  [1] (ℹ️)       information
+2194..2199    ; Extended_Pictographic#  1.1  [6] (↔️..↙️)    left-right arrow..down-left arrow
+21A9..21AA    ; Extended_Pictographic#  1.1  [2] (↩️..↪️)    right arrow curving left..left arrow curving right
+231A..231B    ; Extended_Pictographic#  1.1  [2] (⌚..⌛)    watch..hourglass done
+2328          ; Extended_Pictographic#  1.1  [1] (⌨️)       keyboard
+2388          ; Extended_Pictographic#  3.0  [1] (⎈️)       HELM SYMBOL
+23CF          ; Extended_Pictographic#  4.0  [1] (⏏️)       eject button
+23E9..23F3    ; Extended_Pictographic#  6.0 [11] (⏩..⏳)    fast-forward button..hourglass not done
+23F8..23FA    ; Extended_Pictographic#  7.0  [3] (⏸️..⏺️)    pause button..record button
+24C2          ; Extended_Pictographic#  1.1  [1] (Ⓜ️)       circled M
+25AA..25AB    ; Extended_Pictographic#  1.1  [2] (▪️..▫️)    black small square..white small square
+25B6          ; Extended_Pictographic#  1.1  [1] (▶️)       play button
+25C0          ; Extended_Pictographic#  1.1  [1] (◀️)       reverse button
+25FB..25FE    ; Extended_Pictographic#  3.2  [4] (◻️..◾)    white medium square..black medium-small square
+2600..2605    ; Extended_Pictographic#  1.1  [6] (☀️..★️)    sun..BLACK STAR
+2607..2612    ; Extended_Pictographic#  1.1 [12] (☇️..☒️)    LIGHTNING..BALLOT BOX WITH X
+2614..2615    ; Extended_Pictographic#  4.0  [2] (☔..☕)    umbrella with rain drops..hot beverage
+2616..2617    ; Extended_Pictographic#  3.2  [2] (☖️..☗️)    WHITE SHOGI PIECE..BLACK SHOGI PIECE
+2618          ; Extended_Pictographic#  4.1  [1] (☘️)       shamrock
+2619          ; Extended_Pictographic#  3.0  [1] (☙️)       REVERSED ROTATED FLORAL HEART BULLET
+261A..266F    ; Extended_Pictographic#  1.1 [86] (☚️..♯️)    BLACK LEFT POINTING INDEX..MUSIC SHARP SIGN
+2670..2671    ; Extended_Pictographic#  3.0  [2] (♰️..♱️)    WEST SYRIAC CROSS..EAST SYRIAC CROSS
+2672..267D    ; Extended_Pictographic#  3.2 [12] (♲️..♽️)    UNIVERSAL RECYCLING SYMBOL..PARTIALLY-RECYCLED PAPER SYMBOL
+267E..267F    ; Extended_Pictographic#  4.1  [2] (♾️..♿)    infinity..wheelchair symbol
+2680..2685    ; Extended_Pictographic#  3.2  [6] (⚀️..⚅️)    DIE FACE-1..DIE FACE-6
+2690..2691    ; Extended_Pictographic#  4.0  [2] (⚐️..⚑️)    WHITE FLAG..BLACK FLAG
+2692..269C    ; Extended_Pictographic#  4.1 [11] (⚒️..⚜️)    hammer and pick..fleur-de-lis
+269D          ; Extended_Pictographic#  5.1  [1] (⚝️)       OUTLINED WHITE STAR
+269E..269F    ; Extended_Pictographic#  5.2  [2] (⚞️..⚟️)    THREE LINES CONVERGING RIGHT..THREE LINES CONVERGING LEFT
+26A0..26A1    ; Extended_Pictographic#  4.0  [2] (⚠️..⚡)    warning..high voltage
+26A2..26B1    ; Extended_Pictographic#  4.1 [16] (⚢️..⚱️)    DOUBLED FEMALE SIGN..funeral urn
+26B2          ; Extended_Pictographic#  5.0  [1] (⚲️)       NEUTER
+26B3..26BC    ; Extended_Pictographic#  5.1 [10] (⚳️..⚼️)    CERES..SESQUIQUADRATE
+26BD..26BF    ; Extended_Pictographic#  5.2  [3] (⚽..⚿️)    soccer ball..SQUARED KEY
+26C0..26C3    ; Extended_Pictographic#  5.1  [4] (⛀️..⛃️)    WHITE DRAUGHTS MAN..BLACK DRAUGHTS KING
+26C4..26CD    ; Extended_Pictographic#  5.2 [10] (⛄..⛍️)    snowman without snow..DISABLED CAR
+26CE          ; Extended_Pictographic#  6.0  [1] (⛎)       Ophiuchus
+26CF..26E1    ; Extended_Pictographic#  5.2 [19] (⛏️..⛡️)    pick..RESTRICTED LEFT ENTRY-2
+26E2          ; Extended_Pictographic#  6.0  [1] (⛢️)       ASTRONOMICAL SYMBOL FOR URANUS
+26E3          ; Extended_Pictographic#  5.2  [1] (⛣️)       HEAVY CIRCLE WITH STROKE AND TWO DOTS ABOVE
+26E4..26E7    ; Extended_Pictographic#  6.0  [4] (⛤️..⛧️)    PENTAGRAM..INVERTED PENTAGRAM
+26E8..26FF    ; Extended_Pictographic#  5.2 [24] (⛨️..⛿️)    BLACK CROSS ON SHIELD..WHITE FLAG WITH HORIZONTAL MIDDLE BLACK STRIPE
+2700          ; Extended_Pictographic#  7.0  [1] (✀️)       BLACK SAFETY SCISSORS
+2701..2704    ; Extended_Pictographic#  1.1  [4] (✁️..✄️)    UPPER BLADE SCISSORS..WHITE SCISSORS
+2705          ; Extended_Pictographic#  6.0  [1] (✅)       white heavy check mark
+2708..2709    ; Extended_Pictographic#  1.1  [2] (✈️..✉️)    airplane..envelope
+270A..270B    ; Extended_Pictographic#  6.0  [2] (✊..✋)    raised fist..raised hand
+270C..2712    ; Extended_Pictographic#  1.1  [7] (✌️..✒️)    victory hand..black nib
+2714          ; Extended_Pictographic#  1.1  [1] (✔️)       heavy check mark
+2716          ; Extended_Pictographic#  1.1  [1] (✖️)       heavy multiplication x
+271D          ; Extended_Pictographic#  1.1  [1] (✝️)       latin cross
+2721          ; Extended_Pictographic#  1.1  [1] (✡️)       star of David
+2728          ; Extended_Pictographic#  6.0  [1] (✨)       sparkles
+2733..2734    ; Extended_Pictographic#  1.1  [2] (✳️..✴️)    eight-spoked asterisk..eight-pointed star
+2744          ; Extended_Pictographic#  1.1  [1] (❄️)       snowflake
+2747          ; Extended_Pictographic#  1.1  [1] (❇️)       sparkle
+274C          ; Extended_Pictographic#  6.0  [1] (❌)       cross mark
+274E          ; Extended_Pictographic#  6.0  [1] (❎)       cross mark button
+2753..2755    ; Extended_Pictographic#  6.0  [3] (❓..❕)    question mark..white exclamation mark
+2757          ; Extended_Pictographic#  5.2  [1] (❗)       exclamation mark
+2763..2767    ; Extended_Pictographic#  1.1  [5] (❣️..❧️)    heavy heart exclamation..ROTATED FLORAL HEART BULLET
+2795..2797    ; Extended_Pictographic#  6.0  [3] (➕..➗)    heavy plus sign..heavy division sign
+27A1          ; Extended_Pictographic#  1.1  [1] (➡️)       right arrow
+27B0          ; Extended_Pictographic#  6.0  [1] (➰)       curly loop
+27BF          ; Extended_Pictographic#  6.0  [1] (➿)       double curly loop
+2934..2935    ; Extended_Pictographic#  3.2  [2] (⤴️..⤵️)    right arrow curving up..right arrow curving down
+2B05..2B07    ; Extended_Pictographic#  4.0  [3] (⬅️..⬇️)    left arrow..down arrow
+2B1B..2B1C    ; Extended_Pictographic#  5.1  [2] (⬛..⬜)    black large square..white large square
+2B50          ; Extended_Pictographic#  5.1  [1] (⭐)       star
+2B55          ; Extended_Pictographic#  5.2  [1] (⭕)       heavy large circle
+3030          ; Extended_Pictographic#  1.1  [1] (〰️)       wavy dash
+303D          ; Extended_Pictographic#  3.2  [1] (〽️)       part alternation mark
+3297          ; Extended_Pictographic#  1.1  [1] (㊗️)       Japanese “congratulations” button
+3299          ; Extended_Pictographic#  1.1  [1] (㊙️)       Japanese “secret” button
+1F000..1F02B  ; Extended_Pictographic#  5.1 [44] (🀀️..🀫️)    MAHJONG TILE EAST WIND..MAHJONG TILE BACK
+1F02C..1F02F  ; Extended_Pictographic#   NA  [4] (🀬️..🀯️)    <reserved-1F02C>..<reserved-1F02F>
+1F030..1F093  ; Extended_Pictographic#  5.1[100] (🀰️..🂓️)    DOMINO TILE HORIZONTAL BACK..DOMINO TILE VERTICAL-06-06
+1F094..1F09F  ; Extended_Pictographic#   NA [12] (🂔️..🂟️)    <reserved-1F094>..<reserved-1F09F>
+1F0A0..1F0AE  ; Extended_Pictographic#  6.0 [15] (🂠️..🂮️)    PLAYING CARD BACK..PLAYING CARD KING OF SPADES
+1F0AF..1F0B0  ; Extended_Pictographic#   NA  [2] (🂯️..🂰️)    <reserved-1F0AF>..<reserved-1F0B0>
+1F0B1..1F0BE  ; Extended_Pictographic#  6.0 [14] (🂱️..🂾️)    PLAYING CARD ACE OF HEARTS..PLAYING CARD KING OF HEARTS
+1F0BF         ; Extended_Pictographic#  7.0  [1] (🂿️)       PLAYING CARD RED JOKER
+1F0C0         ; Extended_Pictographic#   NA  [1] (🃀️)       <reserved-1F0C0>
+1F0C1..1F0CF  ; Extended_Pictographic#  6.0 [15] (🃁️..🃏)    PLAYING CARD ACE OF DIAMONDS..joker
+1F0D0         ; Extended_Pictographic#   NA  [1] (🃐️)       <reserved-1F0D0>
+1F0D1..1F0DF  ; Extended_Pictographic#  6.0 [15] (🃑️..🃟️)    PLAYING CARD ACE OF CLUBS..PLAYING CARD WHITE JOKER
+1F0E0..1F0F5  ; Extended_Pictographic#  7.0 [22] (🃠️..🃵️)    PLAYING CARD FOOL..PLAYING CARD TRUMP-21
+1F0F6..1F0FF  ; Extended_Pictographic#   NA [10] (🃶️..🃿️)    <reserved-1F0F6>..<reserved-1F0FF>
+1F10D..1F10F  ; Extended_Pictographic#   NA  [3] (🄍️..🄏️)    <reserved-1F10D>..<reserved-1F10F>
+1F12F         ; Extended_Pictographic# 11.0  [1] (🄯️)       COPYLEFT SYMBOL
+1F16C..1F16F  ; Extended_Pictographic#   NA  [4] (🅬️..🅯️)    <reserved-1F16C>..<reserved-1F16F>
+1F170..1F171  ; Extended_Pictographic#  6.0  [2] (🅰️..🅱️)    A button (blood type)..B button (blood type)
+1F17E         ; Extended_Pictographic#  6.0  [1] (🅾️)       O button (blood type)
+1F17F         ; Extended_Pictographic#  5.2  [1] (🅿️)       P button
+1F18E         ; Extended_Pictographic#  6.0  [1] (🆎)       AB button (blood type)
+1F191..1F19A  ; Extended_Pictographic#  6.0 [10] (🆑..🆚)    CL button..VS button
+1F1AD..1F1E5  ; Extended_Pictographic#   NA [57] (🆭️..🇥️)    <reserved-1F1AD>..<reserved-1F1E5>
+1F201..1F202  ; Extended_Pictographic#  6.0  [2] (🈁..🈂️)    Japanese “here” button..Japanese “service charge” button
+1F203..1F20F  ; Extended_Pictographic#   NA [13] (🈃️..🈏️)    <reserved-1F203>..<reserved-1F20F>
+1F21A         ; Extended_Pictographic#  5.2  [1] (🈚)       Japanese “free of charge” button
+1F22F         ; Extended_Pictographic#  5.2  [1] (🈯)       Japanese “reserved” button
+1F232..1F23A  ; Extended_Pictographic#  6.0  [9] (🈲..🈺)    Japanese “prohibited” button..Japanese “open for business” button
+1F23C..1F23F  ; Extended_Pictographic#   NA  [4] (🈼️..🈿️)    <reserved-1F23C>..<reserved-1F23F>
+1F249..1F24F  ; Extended_Pictographic#   NA  [7] (🉉️..🉏️)    <reserved-1F249>..<reserved-1F24F>
+1F250..1F251  ; Extended_Pictographic#  6.0  [2] (🉐..🉑)    Japanese “bargain” button..Japanese “acceptable” button
+1F252..1F25F  ; Extended_Pictographic#   NA [14] (🉒️..🉟️)    <reserved-1F252>..<reserved-1F25F>
+1F260..1F265  ; Extended_Pictographic# 10.0  [6] (🉠️..🉥️)    ROUNDED SYMBOL FOR FU..ROUNDED SYMBOL FOR CAI
+1F266..1F2FF  ; Extended_Pictographic#   NA[154] (🉦️..🋿️)    <reserved-1F266>..<reserved-1F2FF>
+1F300..1F320  ; Extended_Pictographic#  6.0 [33] (🌀..🌠)    cyclone..shooting star
+1F321..1F32C  ; Extended_Pictographic#  7.0 [12] (🌡️..🌬️)    thermometer..wind face
+1F32D..1F32F  ; Extended_Pictographic#  8.0  [3] (🌭..🌯)    hot dog..burrito
+1F330..1F335  ; Extended_Pictographic#  6.0  [6] (🌰..🌵)    chestnut..cactus
+1F336         ; Extended_Pictographic#  7.0  [1] (🌶️)       hot pepper
+1F337..1F37C  ; Extended_Pictographic#  6.0 [70] (🌷..🍼)    tulip..baby bottle
+1F37D         ; Extended_Pictographic#  7.0  [1] (🍽️)       fork and knife with plate
+1F37E..1F37F  ; Extended_Pictographic#  8.0  [2] (🍾..🍿)    bottle with popping cork..popcorn
+1F380..1F393  ; Extended_Pictographic#  6.0 [20] (🎀..🎓)    ribbon..graduation cap
+1F394..1F39F  ; Extended_Pictographic#  7.0 [12] (🎔️..🎟️)    HEART WITH TIP ON THE LEFT..admission tickets
+1F3A0..1F3C4  ; Extended_Pictographic#  6.0 [37] (🎠..🏄)    carousel horse..person surfing
+1F3C5         ; Extended_Pictographic#  7.0  [1] (🏅)       sports medal
+1F3C6..1F3CA  ; Extended_Pictographic#  6.0  [5] (🏆..🏊)    trophy..person swimming
+1F3CB..1F3CE  ; Extended_Pictographic#  7.0  [4] (🏋️..🏎️)    person lifting weights..racing car
+1F3CF..1F3D3  ; Extended_Pictographic#  8.0  [5] (🏏..🏓)    cricket game..ping pong
+1F3D4..1F3DF  ; Extended_Pictographic#  7.0 [12] (🏔️..🏟️)    snow-capped mountain..stadium
+1F3E0..1F3F0  ; Extended_Pictographic#  6.0 [17] (🏠..🏰)    house..castle
+1F3F1..1F3F7  ; Extended_Pictographic#  7.0  [7] (🏱️..🏷️)    WHITE PENNANT..label
+1F3F8..1F3FA  ; Extended_Pictographic#  8.0  [3] (🏸..🏺)    badminton..amphora
+1F400..1F43E  ; Extended_Pictographic#  6.0 [63] (🐀..🐾)    rat..paw prints
+1F43F         ; Extended_Pictographic#  7.0  [1] (🐿️)       chipmunk
+1F440         ; Extended_Pictographic#  6.0  [1] (👀)       eyes
+1F441         ; Extended_Pictographic#  7.0  [1] (👁️)       eye
+1F442..1F4F7  ; Extended_Pictographic#  6.0[182] (👂..📷)    ear..camera
+1F4F8         ; Extended_Pictographic#  7.0  [1] (📸)       camera with flash
+1F4F9..1F4FC  ; Extended_Pictographic#  6.0  [4] (📹..📼)    video camera..videocassette
+1F4FD..1F4FE  ; Extended_Pictographic#  7.0  [2] (📽️..📾️)    film projector..PORTABLE STEREO
+1F4FF         ; Extended_Pictographic#  8.0  [1] (📿)       prayer beads
+1F500..1F53D  ; Extended_Pictographic#  6.0 [62] (🔀..🔽)    shuffle tracks button..downwards button
+1F546..1F54A  ; Extended_Pictographic#  7.0  [5] (🕆️..🕊️)    WHITE LATIN CROSS..dove
+1F54B..1F54F  ; Extended_Pictographic#  8.0  [5] (🕋..🕏️)    kaaba..BOWL OF HYGIEIA
+1F550..1F567  ; Extended_Pictographic#  6.0 [24] (🕐..🕧)    one o’clock..twelve-thirty
+1F568..1F579  ; Extended_Pictographic#  7.0 [18] (🕨️..🕹️)    RIGHT SPEAKER..joystick
+1F57A         ; Extended_Pictographic#  9.0  [1] (🕺)       man dancing
+1F57B..1F5A3  ; Extended_Pictographic#  7.0 [41] (🕻️..🖣️)    LEFT HAND TELEPHONE RECEIVER..BLACK DOWN POINTING BACKHAND INDEX
+1F5A4         ; Extended_Pictographic#  9.0  [1] (🖤)       black heart
+1F5A5..1F5FA  ; Extended_Pictographic#  7.0 [86] (🖥️..🗺️)    desktop computer..world map
+1F5FB..1F5FF  ; Extended_Pictographic#  6.0  [5] (🗻..🗿)    mount fuji..moai
+1F600         ; Extended_Pictographic#  6.1  [1] (😀)       grinning face
+1F601..1F610  ; Extended_Pictographic#  6.0 [16] (😁..😐)    beaming face with smiling eyes..neutral face
+1F611         ; Extended_Pictographic#  6.1  [1] (😑)       expressionless face
+1F612..1F614  ; Extended_Pictographic#  6.0  [3] (😒..😔)    unamused face..pensive face
+1F615         ; Extended_Pictographic#  6.1  [1] (😕)       confused face
+1F616         ; Extended_Pictographic#  6.0  [1] (😖)       confounded face
+1F617         ; Extended_Pictographic#  6.1  [1] (😗)       kissing face
+1F618         ; Extended_Pictographic#  6.0  [1] (😘)       face blowing a kiss
+1F619         ; Extended_Pictographic#  6.1  [1] (😙)       kissing face with smiling eyes
+1F61A         ; Extended_Pictographic#  6.0  [1] (😚)       kissing face with closed eyes
+1F61B         ; Extended_Pictographic#  6.1  [1] (😛)       face with tongue
+1F61C..1F61E  ; Extended_Pictographic#  6.0  [3] (😜..😞)    winking face with tongue..disappointed face
+1F61F         ; Extended_Pictographic#  6.1  [1] (😟)       worried face
+1F620..1F625  ; Extended_Pictographic#  6.0  [6] (😠..😥)    angry face..sad but relieved face
+1F626..1F627  ; Extended_Pictographic#  6.1  [2] (😦..😧)    frowning face with open mouth..anguished face
+1F628..1F62B  ; Extended_Pictographic#  6.0  [4] (😨..😫)    fearful face..tired face
+1F62C         ; Extended_Pictographic#  6.1  [1] (😬)       grimacing face
+1F62D         ; Extended_Pictographic#  6.0  [1] (😭)       loudly crying face
+1F62E..1F62F  ; Extended_Pictographic#  6.1  [2] (😮..😯)    face with open mouth..hushed face
+1F630..1F633  ; Extended_Pictographic#  6.0  [4] (😰..😳)    anxious face with sweat..flushed face
+1F634         ; Extended_Pictographic#  6.1  [1] (😴)       sleeping face
+1F635..1F640  ; Extended_Pictographic#  6.0 [12] (😵..🙀)    dizzy face..weary cat face
+1F641..1F642  ; Extended_Pictographic#  7.0  [2] (🙁..🙂)    slightly frowning face..slightly smiling face
+1F643..1F644  ; Extended_Pictographic#  8.0  [2] (🙃..🙄)    upside-down face..face with rolling eyes
+1F645..1F64F  ; Extended_Pictographic#  6.0 [11] (🙅..🙏)    person gesturing NO..folded hands
+1F680..1F6C5  ; Extended_Pictographic#  6.0 [70] (🚀..🛅)    rocket..left luggage
+1F6C6..1F6CF  ; Extended_Pictographic#  7.0 [10] (🛆️..🛏️)    TRIANGLE WITH ROUNDED CORNERS..bed
+1F6D0         ; Extended_Pictographic#  8.0  [1] (🛐)       place of worship
+1F6D1..1F6D2  ; Extended_Pictographic#  9.0  [2] (🛑..🛒)    stop sign..shopping cart
+1F6D3..1F6D4  ; Extended_Pictographic# 10.0  [2] (🛓️..🛔️)    STUPA..PAGODA
+1F6D5..1F6DF  ; Extended_Pictographic#   NA [11] (🛕️..🛟️)    <reserved-1F6D5>..<reserved-1F6DF>
+1F6E0..1F6EC  ; Extended_Pictographic#  7.0 [13] (🛠️..🛬)    hammer and wrench..airplane arrival
+1F6ED..1F6EF  ; Extended_Pictographic#   NA  [3] (🛭️..🛯️)    <reserved-1F6ED>..<reserved-1F6EF>
+1F6F0..1F6F3  ; Extended_Pictographic#  7.0  [4] (🛰️..🛳️)    satellite..passenger ship
+1F6F4..1F6F6  ; Extended_Pictographic#  9.0  [3] (🛴..🛶)    kick scooter..canoe
+1F6F7..1F6F8  ; Extended_Pictographic# 10.0  [2] (🛷..🛸)    sled..flying saucer
+1F6F9         ; Extended_Pictographic# 11.0  [1] (🛹)       skateboard
+1F6FA..1F6FF  ; Extended_Pictographic#   NA  [6] (🛺️..🛿️)    <reserved-1F6FA>..<reserved-1F6FF>
+1F774..1F77F  ; Extended_Pictographic#   NA [12] (🝴️..🝿️)    <reserved-1F774>..<reserved-1F77F>
+1F7D5..1F7D8  ; Extended_Pictographic# 11.0  [4] (🟕️..🟘️)    CIRCLED TRIANGLE..NEGATIVE CIRCLED SQUARE
+1F7D9..1F7FF  ; Extended_Pictographic#   NA [39] (🟙️..🟿️)    <reserved-1F7D9>..<reserved-1F7FF>
+1F80C..1F80F  ; Extended_Pictographic#   NA  [4] (🠌️..🠏️)    <reserved-1F80C>..<reserved-1F80F>
+1F848..1F84F  ; Extended_Pictographic#   NA  [8] (🡈️..🡏️)    <reserved-1F848>..<reserved-1F84F>
+1F85A..1F85F  ; Extended_Pictographic#   NA  [6] (🡚️..🡟️)    <reserved-1F85A>..<reserved-1F85F>
+1F888..1F88F  ; Extended_Pictographic#   NA  [8] (🢈️..🢏️)    <reserved-1F888>..<reserved-1F88F>
+1F8AE..1F8FF  ; Extended_Pictographic#   NA [82] (🢮️..🣿️)    <reserved-1F8AE>..<reserved-1F8FF>
+1F90C..1F90F  ; Extended_Pictographic#   NA  [4] (🤌️..🤏️)    <reserved-1F90C>..<reserved-1F90F>
+1F910..1F918  ; Extended_Pictographic#  8.0  [9] (🤐..🤘)    zipper-mouth face..sign of the horns
+1F919..1F91E  ; Extended_Pictographic#  9.0  [6] (🤙..🤞)    call me hand..crossed fingers
+1F91F         ; Extended_Pictographic# 10.0  [1] (🤟)       love-you gesture
+1F920..1F927  ; Extended_Pictographic#  9.0  [8] (🤠..🤧)    cowboy hat face..sneezing face
+1F928..1F92F  ; Extended_Pictographic# 10.0  [8] (🤨..🤯)    face with raised eyebrow..exploding head
+1F930         ; Extended_Pictographic#  9.0  [1] (🤰)       pregnant woman
+1F931..1F932  ; Extended_Pictographic# 10.0  [2] (🤱..🤲)    breast-feeding..palms up together
+1F933..1F93A  ; Extended_Pictographic#  9.0  [8] (🤳..🤺)    selfie..person fencing
+1F93C..1F93E  ; Extended_Pictographic#  9.0  [3] (🤼..🤾)    people wrestling..person playing handball
+1F93F         ; Extended_Pictographic#   NA  [1] (🤿️)       <reserved-1F93F>
+1F940..1F945  ; Extended_Pictographic#  9.0  [6] (🥀..🥅)    wilted flower..goal net
+1F947..1F94B  ; Extended_Pictographic#  9.0  [5] (🥇..🥋)    1st place medal..martial arts uniform
+1F94C         ; Extended_Pictographic# 10.0  [1] (🥌)       curling stone
+1F94D..1F94F  ; Extended_Pictographic# 11.0  [3] (🥍..🥏)    lacrosse..flying disc
+1F950..1F95E  ; Extended_Pictographic#  9.0 [15] (🥐..🥞)    croissant..pancakes
+1F95F..1F96B  ; Extended_Pictographic# 10.0 [13] (🥟..🥫)    dumpling..canned food
+1F96C..1F970  ; Extended_Pictographic# 11.0  [5] (🥬..🥰)    leafy green..smiling face with 3 hearts
+1F971..1F972  ; Extended_Pictographic#   NA  [2] (🥱️..🥲️)    <reserved-1F971>..<reserved-1F972>
+1F973..1F976  ; Extended_Pictographic# 11.0  [4] (🥳..🥶)    partying face..cold face
+1F977..1F979  ; Extended_Pictographic#   NA  [3] (🥷️..🥹️)    <reserved-1F977>..<reserved-1F979>
+1F97A         ; Extended_Pictographic# 11.0  [1] (🥺)       pleading face
+1F97B         ; Extended_Pictographic#   NA  [1] (🥻️)       <reserved-1F97B>
+1F97C..1F97F  ; Extended_Pictographic# 11.0  [4] (🥼..🥿)    lab coat..woman’s flat shoe
+1F980..1F984  ; Extended_Pictographic#  8.0  [5] (🦀..🦄)    crab..unicorn face
+1F985..1F991  ; Extended_Pictographic#  9.0 [13] (🦅..🦑)    eagle..squid
+1F992..1F997  ; Extended_Pictographic# 10.0  [6] (🦒..🦗)    giraffe..cricket
+1F998..1F9A2  ; Extended_Pictographic# 11.0 [11] (🦘..🦢)    kangaroo..swan
+1F9A3..1F9AF  ; Extended_Pictographic#   NA [13] (🦣️..🦯️)    <reserved-1F9A3>..<reserved-1F9AF>
+1F9B0..1F9B9  ; Extended_Pictographic# 11.0 [10] (🦰..🦹)    red-haired..supervillain
+1F9BA..1F9BF  ; Extended_Pictographic#   NA  [6] (🦺️..🦿️)    <reserved-1F9BA>..<reserved-1F9BF>
+1F9C0         ; Extended_Pictographic#  8.0  [1] (🧀)       cheese wedge
+1F9C1..1F9C2  ; Extended_Pictographic# 11.0  [2] (🧁..🧂)    cupcake..salt
+1F9C3..1F9CF  ; Extended_Pictographic#   NA [13] (🧃️..🧏️)    <reserved-1F9C3>..<reserved-1F9CF>
+1F9D0..1F9E6  ; Extended_Pictographic# 10.0 [23] (🧐..🧦)    face with monocle..socks
+1F9E7..1F9FF  ; Extended_Pictographic# 11.0 [25] (🧧..🧿)    red envelope..nazar amulet
+1FA00..1FA5F  ; Extended_Pictographic#   NA [96] (🨀️..🩟️)    <reserved-1FA00>..<reserved-1FA5F>
+1FA60..1FA6D  ; Extended_Pictographic# 11.0 [14] (🩠️..🩭️)    XIANGQI RED GENERAL..XIANGQI BLACK SOLDIER
+1FA6E..1FFFD  ; Extended_Pictographic#   NA[1424] (🩮️..🿽️)   <reserved-1FA6E>..<reserved-1FFFD>
+
+# Total elements: 3793
+
+#EOF
diff --git a/Project/Tools/emoji-list.txt b/Project/Tools/emoji-list.txt
new file mode 100644
index 0000000..9bc456f
--- /dev/null
+++ b/Project/Tools/emoji-list.txt
@@ -0,0 +1,1027 @@
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+🧱
+🧲
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+✨
+🧨
+❌
+❎
+🧩
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+❔
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+🧪
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+➕
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+🦐
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+➰
+➿
+🧰
diff --git a/Project/Tools/stream_language_detector.py b/Project/Tools/stream_language_detector.py
new file mode 100755
index 0000000..08d4a2e
--- /dev/null
+++ b/Project/Tools/stream_language_detector.py
@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+from langdetect import detect
+import fileinput as fi
+import sys
+
+# just a little script to detect languages of lines starting 
+# with the json keyword 'text' and writing the language as json value to stdout.
+# other lines are just passing through so that this script can be used in a shell pipeline
+
+for line in fi.input():
+    if line.startswith('  "text"'):
+        try:
+            sys.stdout.write('  "lang": "' + detect(line[10:]) + '"\n')
+        except Exception:
+            sys.stdout.write('  "lang": "NaN"\n')
+    sys.stdout.write(line)
+
+
diff --git a/Project/Tools/twitter2messages.sh b/Project/Tools/twitter2messages.sh
new file mode 100755
index 0000000..0920e0a
--- /dev/null
+++ b/Project/Tools/twitter2messages.sh
@@ -0,0 +1,72 @@
+#!/usr/bin/env bash
+
+SCRIPT=`realpath $0`
+SCRIPTPATH=`dirname $SCRIPT`
+
+# toolset:---------------------------------------------------------------------
+
+command 2> >(while read line; do echo -e "\e[01;31m$line\e[0m" >&2; done)
+
+function lineprint {
+    printf '%*s\n' "${COLUMNS:-$(tput cols)}" '' | tr ' ' =
+}
+
+function message {
+    lineprint
+    printf "$1\n"
+    lineprint
+}
+
+function error_message {
+    lineprint
+    printf "$1\n" >&2
+    lineprint
+}
+
+current_action="IDLE"
+
+function confirm_action {
+    message "successfully finished action: $current_action"
+}
+
+function set_action {
+    current_action="$1"
+    message "$1"
+}
+
+function perform {
+    "$@"
+    local status=$?
+    if [ $status -ne 0 ]
+    then
+        error_message "$current_action failed!"
+    fi
+    return $status
+}
+
+function perform_and_exit {
+    perform "$@" || exit 1
+}
+
+# -----------------------------------------------------------------------------
+
+INPUT=$1
+OUTPUT=$2
+if [ $# -ne 2 ]
+then
+    error_message "Error: no input file given. Usage: $0 <Filepath> <outputfile>"
+    exit 1
+fi
+
+set_action "processing all files in $INPUT and write to $OUTPUT"
+
+perform_and_exit export elist=\"`head -c -1 "$SCRIPTPATH/emoji-list.txt" | tr '\n' ',' | sed 's/,/\",\"/g'`\"
+perform_and_exit echo "filter by emoji list:"
+perform_and_exit echo $elist | tr -d '"' | tr -d ','
+
+#perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.name, text: .text} | select(.text != null) | [select(.text | contains($elist))] | select(any)| unique_by(.id) | .[]" | tee /dev/tty > "$OUTPUT"
+perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.id, text: .text, lang: .lang, reply_to: .in_reply_to_status_id} | select(.text != null)" | grep --no-group-separator -Ff "$SCRIPTPATH/emoji-list.txt" -A 3 -B 4 | tee /dev/tty > "$OUTPUT"
+
+# ↑ such obvious, much selfexplaining 💁😈
+
+confirm_action
diff --git a/Project/Tools/whatsapp2csv.sh b/Project/Tools/whatsapp2csv.sh
new file mode 100755
index 0000000..178f170
--- /dev/null
+++ b/Project/Tools/whatsapp2csv.sh
@@ -0,0 +1,85 @@
+#!/usr/bin/env bash
+
+SCRIPT=`realpath $0`
+SCRIPTPATH=`dirname $SCRIPT`
+
+# toolset:---------------------------------------------------------------------
+
+command 2> >(while read line; do echo -e "\e[01;31m$line\e[0m" >&2; done)
+
+function lineprint {
+    printf '%*s\n' "${COLUMNS:-$(tput cols)}" '' | tr ' ' =
+}
+
+function message {
+    lineprint
+    printf "$1\n"
+    lineprint
+}
+
+function error_message {
+    lineprint
+    printf "$1\n" >&2
+    lineprint
+}
+
+current_action="IDLE"
+
+function confirm_action {
+    message "successfully finished action: $current_action"
+}
+
+function set_action {
+    current_action="$1"
+    message "$1"
+}
+
+function perform {
+    "$@"
+    local status=$?
+    if [ $status -ne 0 ]
+    then
+        error_message "$current_action failed!"
+    fi
+    return $status
+}
+
+function perform_and_exit {
+    perform "$@" || exit 1
+}
+
+# -----------------------------------------------------------------------------
+# reading input
+
+INPUT=$1
+OUTPUT="$INPUT.csv"
+
+if [ $# -eq 0 ]
+then
+    error_message "Error: no input file given. Usage: $0 <Whatsapp textbackup file>"
+    exit 1
+fi
+
+set_action "processing File: ${INPUT}"
+
+if [ ! -e "$INPUT" ]
+then
+    error_message "Error: file '$INPUT' not found"
+    exit 1
+fi
+
+perform_and_exit echo -e "date\ttime\tperson\tmessage" > "$OUTPUT"
+
+# doing the following things in the pipeline below:
+#
+# 1. merging multiline messages by replacing newlines with spaces if they are not starting with a number followed by '/' (not stating with a date)
+# 2. delete all lines containing the keywords: "added, creates, end-to-end, Media ommited" (which are most probably system messages)
+# 3. replace separators of whatsapp's txt-format with '\t' as separator for csv
+# 4. delete all double-quotes. Because people are too stupid to type them in pairs and that breaks some csv-interpreters 🤦‍♂
+
+perform_and_exit sed ':a;N;/\n[0-9]\{1,\}\+\//!s/\n/ /;ta;P;D' "$INPUT" | grep -v -E 'left|added|created|end-to-end|Media omitted|import java|<VirtualHost' | sed 's/, /\t/1; s/- /\t/1; s/: /\t/1;' | tr -d '"' >> "$OUTPUT"
+
+confirm_action
+
+message "Wrote output to $OUTPUT"
+
diff --git a/README.md b/README.md
index cd98d00..6fb1680 100644
--- a/README.md
+++ b/README.md
@@ -39,4 +39,8 @@ Repository for the NLP-LAB
 
   * search for predefined Questions in Knowledge Graph
 
+----
 
+## Links
+
+https://github.com/AKSW/NSpM