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nlp-lab/Project/advanced_approach/simple_twitter_learning_old.ipynb
Jonas Weinz a221d9f3b5 renamed a lot of stuff
2018-07-27 12:11:38 +02:00

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{
"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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
"[nltk_data] /home/jonas/nltk_data...\n",
"[nltk_data] Package averaged_perceptron_tagger is already up-to-\n",
"[nltk_data] date!\n",
"[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from IPython.display import clear_output, Markdown, Math\n",
"import ipywidgets as widgets\n",
"import os\n",
"import glob\n",
"import json\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",
"import nltk\n",
"from keras.models import load_model\n",
"from sklearn.externals import joblib\n",
"import pickle\n",
"import operator\n",
"from sklearn.pipeline import Pipeline\n",
"nltk.download('punkt')\n",
"nltk.download('averaged_perceptron_tagger')\n",
"nltk.download('wordnet')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append(\"..\")\n",
"\n",
"from Tools.Emoji_Distance import sentiment_vector_to_emoji\n",
"from Tools.Emoji_Distance import emoji_to_sentiment_vector\n",
"\n",
"def emoji2sent(emoji_arr, only_emoticons=True):\n",
" return np.array([emoji_to_sentiment_vector(e, only_emoticons=only_emoticons) for e in emoji_arr])\n",
"\n",
"def sent2emoji(sent_arr, custom_target_emojis=None, only_emoticons=True):\n",
" return [sentiment_vector_to_emoji(s, custom_target_emojis=custom_target_emojis, only_emoticons=only_emoticons) for s in sent_arr]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"SINGLE_LABEL = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"## classes and functions we are using later:\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* functions for selecting items from a set / list"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def latest(lst):\n",
" return lst[-1] if len(lst) > 0 else 'X' \n",
"def most_common(lst):\n",
" # trying to find the most common used emoji in the given lst\n",
" return max(set(lst), key=lst.count) if len(lst) > 0 else \"X\" # setting label to 'X' if there is an empty emoji list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* our emoji blacklist (skin and sex modifiers)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# defining blacklist for modifier emojis:\n",
"emoji_blacklist = set([\n",
" chr(0x1F3FB),\n",
" chr(0x1F3FC),\n",
" chr(0x1F3FD),\n",
" chr(0x1F3FE),\n",
" chr(0x1F3FF),\n",
" chr(0x2642),\n",
" chr(0x2640)\n",
"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* lemmatization helper functions"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from nltk.stem.snowball import SnowballStemmer\n",
"from nltk.stem import WordNetLemmatizer\n",
"from nltk import pos_tag\n",
"from nltk import word_tokenize\n",
"from nltk.corpus import wordnet\n",
"\n",
"def get_wordnet_pos(treebank_tag):\n",
"\n",
" if treebank_tag.startswith('J'):\n",
" return wordnet.ADJ\n",
" elif treebank_tag.startswith('V'):\n",
" return wordnet.VERB\n",
" elif treebank_tag.startswith('N'):\n",
" return wordnet.NOUN\n",
" elif treebank_tag.startswith('R'):\n",
" return wordnet.ADV\n",
" else:\n",
" return wordnet.NOUN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* the sample data manager loads and preprocesses data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class sample_data_manager(object):\n",
" @staticmethod\n",
" def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None):\n",
" sdm = sample_data_manager(path)\n",
" sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons)\n",
" if apply_stemming:\n",
" sdm.apply_stemming_and_lemmatization()\n",
" \n",
" sdm.generate_emoji_count_and_weights()\n",
" \n",
" if n_top_emojis > 0:\n",
" sdm.filter_by_top_emojis(n_top=n_top_emojis)\n",
" \n",
" return sdm\n",
" \n",
" \n",
" def __init__(self, data_root_folder:str):\n",
" self.data_root_folder = data_root_folder\n",
" self.json_files = sorted(glob.glob(self.data_root_folder + \"/*.json\"))\n",
" self.n_files = len(self.json_files)\n",
" self.raw_data = None\n",
" self.emojis = None\n",
" self.plain_text = None\n",
" self.labels = None\n",
" self.emoji_count = None\n",
" self.emoji_weights = None\n",
" self.X = None\n",
" self.y = None\n",
" self.Xt = None\n",
" self.yt = None\n",
" self.top_emojis = None\n",
" \n",
" def read_files(self, file_index_range:list, only_emoticons=True):\n",
" assert np.min(file_index_range) >= 0 and np.max(file_index_range) < self.n_files\n",
" for i in file_index_range:\n",
" print(\"reading file: \" + self.json_files[i] + \"...\")\n",
" if self.raw_data is None:\n",
" self.raw_data = pd.read_json(self.json_files[i], encoding=\"utf-8\")\n",
" else:\n",
" self.raw_data = self.raw_data.append(pd.read_json(self.json_files[i], encoding=\"utf-8\"))\n",
" \n",
" self.emojis = self.raw_data['EMOJI']\n",
" self.plain_text = self.raw_data['text']\n",
" \n",
" # replacing keywords. TODO: maybe these information can be extracted and used\n",
" self.plain_text = self.plain_text.str.replace(\"(<EMOJI>|<USER>|<HASHTAG>)\",\"\").str.replace(\"[\" + \"\".join(list(emoji_blacklist)) + \"]\",\"\")\n",
" \n",
" # so far filtering for the latest emoji. TODO: maybe there are also better approaches\n",
" self.labels = emoji2sent([latest(e) for e in self.emojis], only_emoticons=only_emoticons )\n",
" \n",
" # and filter out all samples we have no label for:\n",
" wrong_labels = np.isnan(np.linalg.norm(self.labels, axis=1)) \n",
"\n",
" self.labels = self.labels[np.invert(wrong_labels)]\n",
" self.plain_text = self.plain_text[np.invert(wrong_labels)]\n",
" self.emojis = self.emojis[np.invert(wrong_labels)]\n",
" \n",
" print(\"imported \" + str(len(self.labels)) + \" samples\")\n",
" \n",
" def apply_stemming_and_lemmatization(self):\n",
" stemmer = SnowballStemmer(\"english\")\n",
" for key in self.plain_text.keys():\n",
" stemmed_sent = []\n",
" for word in self.plain_text[key].split(\" \"):\n",
" word_stemmed = stemmer.stem(word)\n",
" stemmed_sent.append(word_stemmed)\n",
" stemmed_sent = (\" \").join(stemmed_sent)\n",
" self.plain_text[key] = stemmed_sent\n",
" \n",
" lemmatizer = WordNetLemmatizer()\n",
" for key in self.plain_text.keys():\n",
" lemmatized_sent = []\n",
" sent_pos = pos_tag(word_tokenize(self.plain_text[key]))\n",
" for word in sent_pos:\n",
" wordnet_pos = get_wordnet_pos(word[1].lower())\n",
" word_lemmatized = lemmatizer.lemmatize(word[0], pos=wordnet_pos)\n",
" lemmatized_sent.append(word_lemmatized)\n",
" lemmatized_sent = (\" \").join(lemmatized_sent)\n",
" self.plain_text[key] = lemmatized_sent\n",
" \n",
" def generate_emoji_count_and_weights(self):\n",
" self.emoji_count = {}\n",
" for e_list in self.emojis:\n",
" for e in set(e_list):\n",
" if e not in self.emoji_count:\n",
" self.emoji_count[e] = 0\n",
" self.emoji_count[e] += 1\n",
" \n",
" emoji_sum = sum([self.emoji_count[e] for e in self.emoji_count])\n",
"\n",
" self.emoji_weights = {}\n",
" for e in self.emoji_count:\n",
" # tfidf for emojis\n",
" self.emoji_weights[e] = np.log((emoji_sum / self.emoji_count[e]))\n",
"\n",
" weights_sum= sum([self.emoji_weights[x] for x in self.emoji_weights])\n",
"\n",
" # normalize:\n",
" for e in self.emoji_weights:\n",
" self.emoji_weights[e] = self.emoji_weights[e] / weights_sum\n",
"\n",
" self.emoji_weights['X'] = 0 # dummy values\n",
" self.emoji_count['X'] = 0\n",
" \n",
" def get_emoji_count(self):\n",
" sorted_emoji_count = list(reversed(sorted(self.emoji_count.items(), key=operator.itemgetter(1))))\n",
" #display(sorted_emoji_count)\n",
" return sorted_emoji_count\n",
" \n",
" def filter_by_top_emojis(self,n_top = 20):\n",
" self.top_emojis = [x[0] for x in self.get_emoji_count()[:n_top]]\n",
" in_top = [sentiment_vector_to_emoji(x) in self.top_emojis for x in self.labels]\n",
" self.labels = self.labels[in_top]\n",
" self.plain_text = self.plain_text[in_top]\n",
" self.emojis = self.emojis[in_top]\n",
" print(\"remaining samples after top emoji filtering: \", len(self.labels))\n",
" \n",
" def create_train_test_split(self, split = 0.1, random_state = 4222):\n",
" self.X, self.Xt, self.y, self.yt = train_test_split(self.plain_text, self.labels, test_size=split, random_state=random_state)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* the pipeline manager saves and stores sklearn pipelines. Keras models are handled differently, so the have to be named explicitly during save and load operations"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"class pipeline_manager(object):\n",
" @staticmethod\n",
" def load_pipeline_from_files(file_prefix:str, keras_models = [], all_models = []):\n",
" pm = pipeline_manager(keras_models=keras_models)\n",
" pm.load(file_prefix, all_models)\n",
" return pm\n",
" \n",
" @staticmethod\n",
" def create_keras_pipeline_with_vectorizer(vectorizer, layers, sdm:sample_data_manager):\n",
" '''\n",
" creates pipeline with vectorizer and keras classifier\n",
" '''\n",
" from keras.models import Sequential\n",
" from keras.layers import Dense\n",
" \n",
" if sdm.X is None:\n",
" sdm.create_train_test_split()\n",
" \n",
" vec_train = vectorizer.fit_transform(sdm.X)\n",
" vec_test = vectorizer.transform(sdm.Xt)\n",
" # creating keras model:\n",
" model=Sequential()\n",
" \n",
" keras_layers = []\n",
" first_layer = True\n",
" for layer in layers:\n",
" if first_layer:\n",
" model.add(Dense(units=layer[0], activation=layer[1], input_dim=vectorizer.transform([\" \"])[0]._shape[1]))\n",
" first_layer = False\n",
" else:\n",
" model.add(Dense(units=layer[0], activation=layer[1]))\n",
" \n",
" model.compile(loss='mean_squared_error',\n",
" optimizer='adam')\n",
" \n",
" pipeline = Pipeline([\n",
" ('vectorizer',vectorizer),\n",
" ('keras_model', model)\n",
" ])\n",
" \n",
" return pipeline_manager(pipeline=pipeline, keras_models=['keras_model'])\n",
" \n",
" @staticmethod\n",
" def create_pipeline_with_classifier_and_vectorizer(vectorizer, classifier, sdm:sample_data_manager = None):\n",
" '''\n",
" creates a pipeline with vectorizer and classifier for non keras classifiers\n",
" if sample data manager is given, the vectorizer will be also fitted!\n",
" '''\n",
" if sdm is not None:\n",
" if sdm.X is None:\n",
" sdm.create_train_test_split()\n",
"\n",
" vec_train = vectorizer.fit_transform(sdm.X)\n",
" vec_test = vectorizer.transform(sdm.Xt)\n",
" \n",
" pipeline = Pipeline([\n",
" ('vectorizer',vectorizer),\n",
" ('classifier', classifier)\n",
" ])\n",
" \n",
" return pipeline_manager(pipeline=pipeline, keras_models=[])\n",
" \n",
" def __init__(self, pipeline = None, keras_models = []):\n",
" self.pipeline = pipeline\n",
" self.additional_objects = {}\n",
" self.keras_models = keras_models\n",
" \n",
" def save(self, prefix:str):\n",
" print(self.keras_models)\n",
" # doing this like explained here: https://stackoverflow.com/a/43415459\n",
" for step in self.pipeline.named_steps:\n",
" if step in self.keras_models:\n",
" self.pipeline.named_steps[step].model.save(prefix + \".\" + step)\n",
" else:\n",
" joblib.dump(self.pipeline.named_steps[step], prefix + \".\" + str(step))\n",
" \n",
" load_command = \"pipeline_manager.load_pipeline_from_files( '\"\n",
" load_command += prefix + \"', \" + str(self.keras_models) + \", \"\n",
" load_command += str(list(self.pipeline.named_steps.keys())) + \")\"\n",
" \n",
" import __main__ as main\n",
" if not hasattr(main, '__file__'):\n",
" display(\"saved pipeline. It can be loaded the following way:\")\n",
" display(Markdown(\"> ```\\n\"+load_command+\"\\n```\"))\n",
" else:\n",
" print(\"saved pipeline. It can be loaded the following way:\")\n",
" print(load_command)\n",
" \n",
" \n",
" def load(self, prefix:str, models = []):\n",
" self.pipeline = None\n",
" model_list = []\n",
" for model in models:\n",
" if model in self.keras_models:\n",
" model_list.append((model, load_model(prefix + \".\" + model)))\n",
" else:\n",
" model_list.append((model, joblib.load(prefix+\".\" + model)))\n",
" self.pipeline = Pipeline(model_list)\n",
" \n",
" def fit(self,X,y):\n",
" self.pipeline.fit(X,y)\n",
" \n",
" def predict(self,X):\n",
" return self.pipeline.predict(X)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* the trainer class passes Data from the sample manager to the pipeline manager"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class trainer(object):\n",
" def __init__(self, sdm:sample_data_manager, pm:pipeline_manager):\n",
" self.sdm = sdm\n",
" self.pm = pm\n",
" \n",
" def fit(self, max_size=10000, disabled_fit_steps=['vectorizer']):\n",
" # TODO: make batch fitting available here (eg: continous waiting for data and fitting them)\n",
" if self.sdm.X is None:\n",
" self.sdm.create_train_test_split()\n",
" disabled_fits = {}\n",
" disabled_fit_transforms = {}\n",
" \n",
" named_steps = self.pm.pipeline.named_steps\n",
" \n",
" for s in disabled_fit_steps:\n",
" # now it gets a little bit dirty:\n",
" # replace fit functions we don't want to call again (e.g. for vectorizers)\n",
" disabled_fits[s] = named_steps[s].fit\n",
" disabled_fit_transforms[s] = named_steps[s].fit_transform\n",
" named_steps[s].fit = lambda self, X, y=None: self\n",
" named_steps[s].fit_transform = named_steps[s].transform\n",
" \n",
" self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])\n",
" \n",
" # restore replaced fit functions:\n",
" for s in disabled_fit_steps:\n",
" named_steps[s].fit = disabled_fits[s]\n",
" named_steps[s].fit_transform = disabled_fit_transforms[s]\n",
" \n",
" def test(self):\n",
" '''\n",
" return: prediction:list, teacher:list\n",
" '''\n",
" if self.sdm.X is None:\n",
" self.sdm.create_train_test_split()\n",
" return self.pm.predict(self.sdm.Xt), self.sdm.yt\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"## Train"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* when in notebook environment: run the stuff below:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reading file: ./data_en/2017-11-01.json...\n",
"imported 33368 samples\n",
"remaining samples after top emoji filtering: 26197\n"
]
}
],
"source": [
"import __main__ as main\n",
"if not hasattr(main, '__file__'):\n",
" # we are in an interactive environment (probably in jupyter)\n",
" # load data:\n",
" sdm = sample_data_manager.generate_and_read(path=\"./data_en/\", n_top_emojis=20, file_range=range(1))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"100/100 [==============================] - 3s 27ms/step - loss: 0.1225\n"
]
}
],
"source": [
" #pm = pipeline_manager.create_keras_pipeline_with_vectorizer(vectorizer=TfidfVectorizer(stop_words='english'),\n",
" # layers=[(10000, 'relu'),(5000, 'relu'),(2500, 'relu'),(y1[0].shape[0],None)], sdm=sdm)\n",
" pm = pipeline_manager.create_keras_pipeline_with_vectorizer(vectorizer=TfidfVectorizer(stop_words='english'),\n",
" layers=[(2500, 'relu'),(3,None)], sdm=sdm)\n",
" \n",
" tr = trainer(sdm=sdm, pm=pm)\n",
" tr.fit(100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"## save classifier"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['keras_model']\n"
]
},
{
"data": {
"text/plain": [
"'saved pipeline. It can be loaded the following way:'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"> ```\n",
"pipeline_manager.load_pipeline_from_files( 'custom_classifier', ['keras_model'], ['vectorizer', 'keras_model'])\n",
"```"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.save('custom_classifier')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"## Prediction"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.15801723, 0.11859037, 0.10975348],\n",
" [0.17035495, 0.10913695, 0.09354854],\n",
" [0.11777218, 0.06569621, 0.06620223],\n",
" ...,\n",
" [0.14746301, 0.09480572, 0.08052498],\n",
" [0.15932804, 0.11895895, 0.10343507],\n",
" [0.17135939, 0.1061406 , 0.09402546]], dtype=float32)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([[0.46813021, 0.24716181, 0.28470797],\n",
" [0.46813021, 0.24716181, 0.28470797],\n",
" [0.70401758, 0.05932203, 0.23666039],\n",
" ...,\n",
" [0.46813021, 0.24716181, 0.28470797],\n",
" [0.46813021, 0.24716181, 0.28470797],\n",
" [0.46813021, 0.24716181, 0.28470797]])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"prediction variance: 0.0006294687\n",
"teacher variance: 0.03341702104519965\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>predict</th>\n",
" <th>predicted_sentiment</th>\n",
" <th>teacher</th>\n",
" <th>teacher_sentiment</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>35671</th>\n",
" <td>😢</td>\n",
" <td>[0.15801723301410675, 0.11859036982059479, 0.1...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>i feel like i care so much more in everi situat</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25683</th>\n",
" <td>😢</td>\n",
" <td>[0.1703549474477768, 0.10913695394992828, 0.09...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>i did not meat to add that 2 there ... hav see...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8985</th>\n",
" <td>😢</td>\n",
" <td>[0.1177721843123436, 0.06569620966911316, 0.06...</td>\n",
" <td>😊</td>\n",
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
" <td>never…</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5410</th>\n",
" <td>😢</td>\n",
" <td>[0.18182337284088135, 0.12382747232913971, 0.0...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>lmao on me ! ! ! wtf wa he suppos to say</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62611</th>\n",
" <td>😢</td>\n",
" <td>[0.1786666363477707, 0.11502400785684586, 0.10...</td>\n",
" <td>😊</td>\n",
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
" <td>this dude alway help me get through my school ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" predict predicted_sentiment teacher \\\n",
"35671 😢 [0.15801723301410675, 0.11859036982059479, 0.1... 😂 \n",
"25683 😢 [0.1703549474477768, 0.10913695394992828, 0.09... 😂 \n",
"8985 😢 [0.1177721843123436, 0.06569620966911316, 0.06... 😊 \n",
"5410 😢 [0.18182337284088135, 0.12382747232913971, 0.0... 😂 \n",
"62611 😢 [0.1786666363477707, 0.11502400785684586, 0.10... 😊 \n",
"\n",
" teacher_sentiment \\\n",
"35671 [0.46813021474490496, 0.24716181096977158, 0.2... \n",
"25683 [0.46813021474490496, 0.24716181096977158, 0.2... \n",
"8985 [0.7040175768989329, 0.059322033898305086, 0.2... \n",
"5410 [0.46813021474490496, 0.24716181096977158, 0.2... \n",
"62611 [0.7040175768989329, 0.059322033898305086, 0.2... \n",
"\n",
" text \n",
"35671 i feel like i care so much more in everi situat \n",
"25683 i did not meat to add that 2 there ... hav see... \n",
"8985 never… \n",
"5410 lmao on me ! ! ! wtf wa he suppos to say \n",
"62611 this dude alway help me get through my school ... "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Squared Error: [0.14140389 0.04240099 0.02944344]\n",
"Variance teacher: [0.02183094 0.02513847 0.00285735]\n",
"Variance prediction: [0.00053908 0.00024232 0.00021658]\n"
]
}
],
"source": [
"import __main__ as main\n",
"if not hasattr(main, '__file__'):\n",
" pred, teacher = tr.test()\n",
" \n",
" display(pred)\n",
" display(teacher)\n",
" \n",
" print('prediction variance: ', np.linalg.norm(np.var(pred, axis=0)))\n",
" print('teacher variance: ', np.linalg.norm(np.var(teacher, axis=0)))\n",
" \n",
" # build a dataframe to visualize test results:\n",
" testlist = pd.DataFrame({'text': sdm.Xt, \n",
" 'teacher': sent2emoji(sdm.yt),\n",
" 'teacher_sentiment': sdm.yt.tolist(),\n",
" 'predict': sent2emoji(pred, custom_target_emojis=sdm.top_emojis),\n",
" 'predicted_sentiment': pred.tolist()})\n",
" # display:\n",
" display(testlist.head())\n",
" \n",
" # mean squared error:\n",
" teacher_sentiments = np.array([sample[1]['teacher_sentiment'] for sample in testlist.iterrows()])\n",
" predicted_sentiments = np.array([sample[1]['predicted_sentiment'] for sample in testlist.iterrows()])\n",
"\n",
" mean_squared_error = ((teacher_sentiments - predicted_sentiments)**2).mean(axis=0)\n",
" print(\"Mean Squared Error: \", mean_squared_error)\n",
" print(\"Variance teacher: \", np.var(teacher_sentiments, axis=0))\n",
" print(\"Variance prediction: \", np.var(predicted_sentiments, axis=0))\n",
" \n",
" # save to csv:\n",
" testlist.to_csv('test.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"## Load classifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import __main__ as main\n",
"if not hasattr(main, '__file__'):\n",
" try:\n",
" pm\n",
" except NameError:\n",
" pass\n",
" else:\n",
" del pm # delete existing pipeline manager if ther is one\n",
"\n",
" pm = pipeline_manager.load_pipeline_from_files( 'custom_classifier', ['keras_model'], ['vectorizer', 'keras_model'])\n",
" lookup_emojis = [#'😂',\n",
" '😭',\n",
" '😍',\n",
" '😩',\n",
" '😊',\n",
" '😘',\n",
" '🙏',\n",
" '🙌',\n",
" '😉',\n",
" '😁',\n",
" '😅',\n",
" '😎',\n",
" '😢',\n",
" '😒',\n",
" '😏',\n",
" '😌',\n",
" '😔',\n",
" '😋',\n",
" '😀',\n",
" '😤']\n",
" out = widgets.Output()\n",
"\n",
" t = widgets.Text()\n",
" b = widgets.Button(\n",
" description='get emoji',\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",
" pred = pm.predict([t.value])\n",
"\n",
" display(Markdown(\"# Predicted Emoji \" + str(sent2emoji(pred, lookup_emojis)[0])))\n",
" display(Markdown(\"# Sentiment Vector: $$ \\pmatrix{\" + str(pred[0,0]) +\n",
" \"\\\\\\\\\" + str(pred[0,1]) + \"\\\\\\\\\" + str(pred[0,2]) + \"}$$\"))\n",
"\n",
" b.on_click(handle_submit)\n",
"\n",
" display(t)\n",
" display(widgets.VBox([b, out])) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# simple twitter approach\n",
"*for learning emoji usage by single (in the meaning of unconnected) twitter messages*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## loading train data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Download preprocessed raw data:\n",
" * [here](https://the-cake-is-a-lie.net/nextcloud/index.php/s/MmXFYj6mGoMQoJN) for english\n",
" * [here](https://the-cake-is-a-lie.net/nextcloud/index.php/s/HgqpQ6rFadtWSAt) for german"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data_root_folder = \"./data_en/\" # i created a symlink here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* get all json files in `data_root_folder`"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"json_files = sorted(glob.glob(data_root_folder + \"/*.json\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* so far, only load the first file"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>EMOJI</th>\n",
" <th>HASHTAGS</th>\n",
" <th>LINKED_USER</th>\n",
" <th>datetime</th>\n",
" <th>id</th>\n",
" <th>lang</th>\n",
" <th>person</th>\n",
" <th>reply_to</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[🔥, 👏]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:00</td>\n",
" <td>925716304635547600</td>\n",
" <td>en</td>\n",
" <td>31507978</td>\n",
" <td>NaN</td>\n",
" <td>fashionbombdaily's photo &lt;EMOJI&gt;&lt;EMOJI&gt;🏼</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[🤦]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:00</td>\n",
" <td>925716304664911900</td>\n",
" <td>en</td>\n",
" <td>231994649</td>\n",
" <td>NaN</td>\n",
" <td>It’s scary how on point my horoscope be &lt;EMOJI...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[😄]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:03</td>\n",
" <td>925716317214089200</td>\n",
" <td>en</td>\n",
" <td>2592765104</td>\n",
" <td>NaN</td>\n",
" <td>Woooaaaahhh &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[📷]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:04</td>\n",
" <td>925716321416949800</td>\n",
" <td>en</td>\n",
" <td>278737933</td>\n",
" <td>NaN</td>\n",
" <td>&lt;EMOJI&gt; vivalcli: Portraits by Zhao Guojing an...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[😩, 😩]</td>\n",
" <td>[]</td>\n",
" <td>[@hiphopphiIes]</td>\n",
" <td>2017-11-01 13:29:06</td>\n",
" <td>925716329801310200</td>\n",
" <td>en</td>\n",
" <td>824586253634981900</td>\n",
" <td>9.257162e+17</td>\n",
" <td>&lt;USER&gt; i wanna know too&lt;EMOJI&gt;&lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>[😭, 💓]</td>\n",
" <td>[]</td>\n",
" <td>[@WizMommma]</td>\n",
" <td>2017-11-01 13:29:02</td>\n",
" <td>925716313019965400</td>\n",
" <td>en</td>\n",
" <td>1581953814</td>\n",
" <td>9.257088e+17</td>\n",
" <td>&lt;USER&gt; veda was yoda too &lt;EMOJI&gt;&lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>[😂]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:05</td>\n",
" <td>925716325607133200</td>\n",
" <td>en</td>\n",
" <td>1001999683</td>\n",
" <td>NaN</td>\n",
" <td>I’m less stressed about turning 30 now &lt;EMOJI&gt;...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>[💯]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:07</td>\n",
" <td>925716334008082400</td>\n",
" <td>en</td>\n",
" <td>745222369183043600</td>\n",
" <td>NaN</td>\n",
" <td>Full charged. &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>[🙄]</td>\n",
" <td>[]</td>\n",
" <td>[@SeaDimon, @lsarsour]</td>\n",
" <td>2017-11-01 13:29:09</td>\n",
" <td>925716342401052700</td>\n",
" <td>en</td>\n",
" <td>798557155217539100</td>\n",
" <td>9.257147e+17</td>\n",
" <td>&lt;USER&gt; That’s part of the problem, (they) &lt;USE...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>[😟, 😥, 😢]</td>\n",
" <td>[]</td>\n",
" <td>[@Ian_khetye]</td>\n",
" <td>2017-11-01 13:29:10</td>\n",
" <td>925716346570240000</td>\n",
" <td>en</td>\n",
" <td>744396039126421500</td>\n",
" <td>9.250629e+17</td>\n",
" <td>&lt;USER&gt; got me emotional there&lt;EMOJI&gt;&lt;EMOJI&gt;&lt;EM...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>[🌻]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:13</td>\n",
" <td>925716359182520300</td>\n",
" <td>en</td>\n",
" <td>721490010118205400</td>\n",
" <td>8.965900e+17</td>\n",
" <td>back to the yellow &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>[🍁, 🌺, 🍂]</td>\n",
" <td>[]</td>\n",
" <td>[@Dimafadma]</td>\n",
" <td>2017-11-01 13:29:15</td>\n",
" <td>925716367558545400</td>\n",
" <td>en</td>\n",
" <td>520536723</td>\n",
" <td>9.257159e+17</td>\n",
" <td>&lt;USER&gt; Happy month to you and your loved ones ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>[🍃]</td>\n",
" <td>[#mortdale, #partofthefamily, #gorgeousboy]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:16</td>\n",
" <td>925716371735900200</td>\n",
" <td>en</td>\n",
" <td>850852815941517300</td>\n",
" <td>NaN</td>\n",
" <td>Maxx and Patricia. Family hangs at For Good He...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>[💭, 🤦]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:20</td>\n",
" <td>925716388513230800</td>\n",
" <td>en</td>\n",
" <td>914145041588867100</td>\n",
" <td>NaN</td>\n",
" <td>I need to STOP beating myself up with my thoug...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>[😍, 😘]</td>\n",
" <td>[#7YearsOfKMH2]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:20</td>\n",
" <td>925716388525645800</td>\n",
" <td>en</td>\n",
" <td>2425405622</td>\n",
" <td>NaN</td>\n",
" <td>Cutest Son &lt;EMOJI&gt;Roll no. 31 &lt;EMOJI&gt; &lt;HASHTAG&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>[😜]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:22</td>\n",
" <td>925716396931240000</td>\n",
" <td>en</td>\n",
" <td>4614871873</td>\n",
" <td>NaN</td>\n",
" <td>By the summer I should have everything up and ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>[😂, 🔥]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:23</td>\n",
" <td>925716401125331000</td>\n",
" <td>en</td>\n",
" <td>2831608345</td>\n",
" <td>NaN</td>\n",
" <td>I know my English is not that good but that do...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>[💕]</td>\n",
" <td>[]</td>\n",
" <td>[@yungbabytate]</td>\n",
" <td>2017-11-01 13:29:23</td>\n",
" <td>925716401133948900</td>\n",
" <td>en</td>\n",
" <td>788571974633009200</td>\n",
" <td>9.255778e+17</td>\n",
" <td>&lt;USER&gt; I &lt;EMOJI&gt; u mama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>[😉]</td>\n",
" <td>[]</td>\n",
" <td>[@cmckenney]</td>\n",
" <td>2017-11-01 13:29:23</td>\n",
" <td>925716401125544000</td>\n",
" <td>en</td>\n",
" <td>218307802</td>\n",
" <td>9.257115e+17</td>\n",
" <td>&lt;USER&gt; That picture was NOT taken this morning...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>[👅]</td>\n",
" <td>[#footfetishnation]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:25</td>\n",
" <td>925716409489002500</td>\n",
" <td>en</td>\n",
" <td>885261166146179100</td>\n",
" <td>NaN</td>\n",
" <td>Welcome to &lt;HASHTAG&gt; &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>[👌, 🙂]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:25</td>\n",
" <td>925716409497272300</td>\n",
" <td>en</td>\n",
" <td>831437760833609700</td>\n",
" <td>NaN</td>\n",
" <td>Awkward &lt;EMOJI&gt;&lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>[🤗, 📺]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:26</td>\n",
" <td>925716413699854300</td>\n",
" <td>en</td>\n",
" <td>231664542</td>\n",
" <td>NaN</td>\n",
" <td>back at it with supernatural &lt;EMOJI&gt;&lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>[💯]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:26</td>\n",
" <td>925716413679009800</td>\n",
" <td>en</td>\n",
" <td>3196847035</td>\n",
" <td>NaN</td>\n",
" <td>One of the best things I've learned was to sto...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>[👅, 💦, 🍑]</td>\n",
" <td>[]</td>\n",
" <td>[@ctrlpurp]</td>\n",
" <td>2017-11-01 13:29:29</td>\n",
" <td>925716426278735900</td>\n",
" <td>en</td>\n",
" <td>918492858352635900</td>\n",
" <td>9.257161e+17</td>\n",
" <td>&lt;USER&gt; Can I taste?&lt;EMOJI&gt;&lt;EMOJI&gt;&lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>[💔]</td>\n",
" <td>[]</td>\n",
" <td>[@saunders_court1]</td>\n",
" <td>2017-11-01 13:29:30</td>\n",
" <td>925716430473039900</td>\n",
" <td>en</td>\n",
" <td>3471187337</td>\n",
" <td>9.257163e+17</td>\n",
" <td>&lt;USER&gt; we miss you ☹️&lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>[🤐, 🤐, 🤐]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:31</td>\n",
" <td>925716434667184100</td>\n",
" <td>en</td>\n",
" <td>780060488600199200</td>\n",
" <td>NaN</td>\n",
" <td>Actually my bias in WJSN are Eunseo &amp;amp; Bona...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>[😴]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:32</td>\n",
" <td>925716438853345300</td>\n",
" <td>en</td>\n",
" <td>388380690</td>\n",
" <td>NaN</td>\n",
" <td>I so cannot be bothered with the rest of the d...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>[😂]</td>\n",
" <td>[]</td>\n",
" <td>[@xxxtentacion]</td>\n",
" <td>2017-11-01 13:29:35</td>\n",
" <td>925716451457163300</td>\n",
" <td>en</td>\n",
" <td>899320696869974000</td>\n",
" <td>NaN</td>\n",
" <td>&lt;USER&gt; 2lit4life&lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>[😂, 🙄]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 13:29:35</td>\n",
" <td>925716451461357600</td>\n",
" <td>en</td>\n",
" <td>784790670</td>\n",
" <td>NaN</td>\n",
" <td>I’m not stop saying that!&lt;EMOJI&gt;&lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>[🎉, 🎂, 🎈, 🎊, 🎁, 💜]</td>\n",
" <td>[]</td>\n",
" <td>[@justinerooney_]</td>\n",
" <td>2017-11-01 13:29:37</td>\n",
" <td>925716459828936700</td>\n",
" <td>en</td>\n",
" <td>3051266655</td>\n",
" <td>NaN</td>\n",
" <td>&lt;USER&gt; HAPPY BIHDAY &lt;EMOJI&gt;&lt;EMOJI&gt;&lt;EMOJI&gt;&lt;EMOJ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68703</th>\n",
" <td>[😕]</td>\n",
" <td>[#halloweencostumes]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:04</td>\n",
" <td>925624214522036200</td>\n",
" <td>en</td>\n",
" <td>1672876458</td>\n",
" <td>NaN</td>\n",
" <td>&lt;HASHTAG&gt; this one falls under the weird crazy...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68704</th>\n",
" <td>[😂, 😩]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:05</td>\n",
" <td>925624218682777600</td>\n",
" <td>en</td>\n",
" <td>382473866</td>\n",
" <td>NaN</td>\n",
" <td>I'm not allowed to have chocolates yet, then I...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68705</th>\n",
" <td>[😂]</td>\n",
" <td>[#MUFC]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:06</td>\n",
" <td>925624222889766900</td>\n",
" <td>en</td>\n",
" <td>893145405457911800</td>\n",
" <td>NaN</td>\n",
" <td>Manchester United manager Mourinho slams 'spec...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68706</th>\n",
" <td>[💖]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:07</td>\n",
" <td>925624227088121900</td>\n",
" <td>en</td>\n",
" <td>240378516</td>\n",
" <td>NaN</td>\n",
" <td>&lt;EMOJI&gt; en Bushwhick</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68707</th>\n",
" <td>[🌆, 👉, 🚖, 📞]</td>\n",
" <td>[#BurkeCentre]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:18</td>\n",
" <td>925624273237983200</td>\n",
" <td>en</td>\n",
" <td>784620573209002000</td>\n",
" <td>NaN</td>\n",
" <td>: &lt;HASHTAG&gt; &lt;EMOJI&gt; &lt;EMOJI&gt;&lt;EMOJI&gt; For Taxi &lt;E...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68708</th>\n",
" <td>[😁]</td>\n",
" <td>[]</td>\n",
" <td>[@mychosliaheart, @BarrettoJulia, @iamjoshuaga...</td>\n",
" <td>2017-11-01 07:23:18</td>\n",
" <td>925624273212805100</td>\n",
" <td>en</td>\n",
" <td>170998187</td>\n",
" <td>9.254136e+17</td>\n",
" <td>&lt;USER&gt; &lt;USER&gt; &lt;USER&gt; Look, Mammeh and Daddeh! ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68709</th>\n",
" <td>[😋]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:24</td>\n",
" <td>925624298395533300</td>\n",
" <td>en</td>\n",
" <td>1348667816</td>\n",
" <td>NaN</td>\n",
" <td>Life is so good with you &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68710</th>\n",
" <td>[👌, 🎃, 😘]</td>\n",
" <td>[#portlandoregon, #portlandhalloween, #carrie…]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:24</td>\n",
" <td>925624298378801200</td>\n",
" <td>en</td>\n",
" <td>722481645765300200</td>\n",
" <td>NaN</td>\n",
" <td>Happy Halloween! &lt;EMOJI&gt;🏽&lt;EMOJI&gt;&lt;EMOJI&gt; &lt;HASHT...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68711</th>\n",
" <td>[👍]</td>\n",
" <td>[]</td>\n",
" <td>[@8limbsbondi...]</td>\n",
" <td>2017-11-01 07:23:26</td>\n",
" <td>925624306779897900</td>\n",
" <td>en</td>\n",
" <td>2443251500</td>\n",
" <td>NaN</td>\n",
" <td>Some work on the ropes in today’s boxing class...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68712</th>\n",
" <td>[😭]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:27</td>\n",
" <td>925624310974136300</td>\n",
" <td>en</td>\n",
" <td>2406186390</td>\n",
" <td>NaN</td>\n",
" <td>Scotty and Kristen’s halloween costumes &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68713</th>\n",
" <td>[😂]</td>\n",
" <td>[]</td>\n",
" <td>[@rfrandrea, @AdaaanAndyyy]</td>\n",
" <td>2017-11-01 07:23:30</td>\n",
" <td>925624323557146600</td>\n",
" <td>en</td>\n",
" <td>1039448149</td>\n",
" <td>9.256150e+17</td>\n",
" <td>&lt;USER&gt; &lt;USER&gt; May pre-month celebration sis &lt;E...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68714</th>\n",
" <td>[😭, 😭, 💘]</td>\n",
" <td>[]</td>\n",
" <td>[@peachshua1230]</td>\n",
" <td>2017-11-01 07:23:31</td>\n",
" <td>925624327755591700</td>\n",
" <td>en</td>\n",
" <td>845085544589672400</td>\n",
" <td>9.256009e+17</td>\n",
" <td>&lt;USER&gt; Awww &lt;EMOJI&gt; Ajsksjdjd im smiling like ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68715</th>\n",
" <td>[😫, ✋]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:34</td>\n",
" <td>925624340342812700</td>\n",
" <td>en</td>\n",
" <td>924752524871131100</td>\n",
" <td>NaN</td>\n",
" <td>I hate when I send a text or snap n I'm so anx...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68716</th>\n",
" <td>[😂, 🙁]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:34</td>\n",
" <td>925624340355280900</td>\n",
" <td>en</td>\n",
" <td>419493819</td>\n",
" <td>NaN</td>\n",
" <td>The answer is no I have no plans and I never l...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68717</th>\n",
" <td>[😭]</td>\n",
" <td>[]</td>\n",
" <td>[@BeachBoy_Gab]</td>\n",
" <td>2017-11-01 07:23:34</td>\n",
" <td>925624340346937300</td>\n",
" <td>en</td>\n",
" <td>2457745952</td>\n",
" <td>9.256219e+17</td>\n",
" <td>&lt;USER&gt; LMAOOO I'm so proud &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68718</th>\n",
" <td>[😢, 💔]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:34</td>\n",
" <td>925624340338507800</td>\n",
" <td>en</td>\n",
" <td>1955767531</td>\n",
" <td>NaN</td>\n",
" <td>My cousin/little sister is leaving to San Fran...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68719</th>\n",
" <td>[🙃]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:35</td>\n",
" <td>925624344524361700</td>\n",
" <td>en</td>\n",
" <td>796490344581898200</td>\n",
" <td>NaN</td>\n",
" <td>Can't be alone w my thoughts tonight so just g...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68720</th>\n",
" <td>[😂, 😂, 😂, 😂, 😂]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:36</td>\n",
" <td>925624348710285300</td>\n",
" <td>en</td>\n",
" <td>907808317124177900</td>\n",
" <td>NaN</td>\n",
" <td>&lt;EMOJI&gt;&lt;EMOJI&gt;&lt;EMOJI&gt;&lt;EMOJI&gt;&lt;EMOJI&gt; ambot!!!</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68721</th>\n",
" <td>[👌, 👊, 🙌]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:37</td>\n",
" <td>925624352929910800</td>\n",
" <td>en</td>\n",
" <td>262162415</td>\n",
" <td>NaN</td>\n",
" <td>&lt;EMOJI&gt;🏽&lt;EMOJI&gt;🏽 1st of the month!!Happy 1st o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68722</th>\n",
" <td>[😴]</td>\n",
" <td>[#WednesdayWisdom]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:41</td>\n",
" <td>925624369715515400</td>\n",
" <td>en</td>\n",
" <td>574882525</td>\n",
" <td>NaN</td>\n",
" <td>&lt;HASHTAG&gt; ... stay in bed &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68723</th>\n",
" <td>[😂, 😂, 😂, 😂]</td>\n",
" <td>[]</td>\n",
" <td>[@Louis_Tomlinson, @NiallOfficial]</td>\n",
" <td>2017-11-01 07:23:44</td>\n",
" <td>925624382269124600</td>\n",
" <td>en</td>\n",
" <td>556175173</td>\n",
" <td>9.254038e+17</td>\n",
" <td>&lt;USER&gt; &lt;USER&gt; THIS IS GOLD. GOLD. &lt;EMOJI&gt;&lt;EMOJ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68724</th>\n",
" <td>[😀]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:45</td>\n",
" <td>925624386455031800</td>\n",
" <td>en</td>\n",
" <td>1610265588</td>\n",
" <td>NaN</td>\n",
" <td>&lt;EMOJI&gt; thank you for the kind compliment</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68725</th>\n",
" <td>[😎]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:45</td>\n",
" <td>925624386454937600</td>\n",
" <td>en</td>\n",
" <td>4760724450</td>\n",
" <td>NaN</td>\n",
" <td>Enjoyed the silence &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68726</th>\n",
" <td>[✨]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:46</td>\n",
" <td>925624390657572900</td>\n",
" <td>en</td>\n",
" <td>882858115636514800</td>\n",
" <td>NaN</td>\n",
" <td>OS: Spiderman Homecoming &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68727</th>\n",
" <td>[🤷]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:46</td>\n",
" <td>925624390682849300</td>\n",
" <td>en</td>\n",
" <td>188129628</td>\n",
" <td>NaN</td>\n",
" <td>Go to hell &lt;EMOJI&gt;🏽‍♀️</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68728</th>\n",
" <td>[😘]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:46</td>\n",
" <td>925624390666129400</td>\n",
" <td>en</td>\n",
" <td>2473135939</td>\n",
" <td>NaN</td>\n",
" <td>Thank you Yomi! &lt;EMOJI&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68729</th>\n",
" <td>[😂]</td>\n",
" <td>[]</td>\n",
" <td>[@discopiggu]</td>\n",
" <td>2017-11-01 07:23:46</td>\n",
" <td>925624390670106600</td>\n",
" <td>en</td>\n",
" <td>2373584209</td>\n",
" <td>9.256241e+17</td>\n",
" <td>&lt;USER&gt; Lol. Just enjoy the stars. Music Kidhar...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68730</th>\n",
" <td>[🙏]</td>\n",
" <td>[#NYCStrong]</td>\n",
" <td>[]</td>\n",
" <td>2017-11-01 07:23:50</td>\n",
" <td>925624407459971100</td>\n",
" <td>en</td>\n",
" <td>181689756</td>\n",
" <td>NaN</td>\n",
" <td>Thoughts and prayers for NY&lt;EMOJI&gt;🏻 &lt;HASHTAG&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68731</th>\n",
" <td>[💁]</td>\n",
" <td>[#GreatSuccess]</td>\n",
" <td>[@BrianyH]</td>\n",
" <td>2017-11-01 07:23:50</td>\n",
" <td>925624407460057100</td>\n",
" <td>en</td>\n",
" <td>60160788</td>\n",
" <td>9.254610e+17</td>\n",
" <td>&lt;USER&gt; I searched COCK, PENIS, SHLONG, WINKY, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68732</th>\n",
" <td>[🍃, 🌻, 🌻, 🍃, 🍃, 💐, 💐, 🍃, 🙋]</td>\n",
" <td>[]</td>\n",
" <td>[@amitbarman520]</td>\n",
" <td>2017-11-01 07:23:53</td>\n",
" <td>925624420022063100</td>\n",
" <td>en</td>\n",
" <td>3792290725</td>\n",
" <td>9.256215e+17</td>\n",
" <td>&lt;USER&gt; Thank you so much&lt;EMOJI&gt;&lt;EMOJI&gt;&lt;EMOJI&gt;&lt;...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>68733 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" EMOJI \\\n",
"0 [🔥, 👏] \n",
"1 [🤦] \n",
"2 [😄] \n",
"3 [📷] \n",
"4 [😩, 😩] \n",
"5 [😭, 💓] \n",
"6 [😂] \n",
"7 [💯] \n",
"8 [🙄] \n",
"9 [😟, 😥, 😢] \n",
"10 [🌻] \n",
"11 [🍁, 🌺, 🍂] \n",
"12 [🍃] \n",
"13 [💭, 🤦] \n",
"14 [😍, 😘] \n",
"15 [😜] \n",
"16 [😂, 🔥] \n",
"17 [💕] \n",
"18 [😉] \n",
"19 [👅] \n",
"20 [👌, 🙂] \n",
"21 [🤗, 📺] \n",
"22 [💯] \n",
"23 [👅, 💦, 🍑] \n",
"24 [💔] \n",
"25 [🤐, 🤐, 🤐] \n",
"26 [😴] \n",
"27 [😂] \n",
"28 [😂, 🙄] \n",
"29 [🎉, 🎂, 🎈, 🎊, 🎁, 💜] \n",
"... ... \n",
"68703 [😕] \n",
"68704 [😂, 😩] \n",
"68705 [😂] \n",
"68706 [💖] \n",
"68707 [🌆, 👉, 🚖, 📞] \n",
"68708 [😁] \n",
"68709 [😋] \n",
"68710 [👌, 🎃, 😘] \n",
"68711 [👍] \n",
"68712 [😭] \n",
"68713 [😂] \n",
"68714 [😭, 😭, 💘] \n",
"68715 [😫, ✋] \n",
"68716 [😂, 🙁] \n",
"68717 [😭] \n",
"68718 [😢, 💔] \n",
"68719 [🙃] \n",
"68720 [😂, 😂, 😂, 😂, 😂] \n",
"68721 [👌, 👊, 🙌] \n",
"68722 [😴] \n",
"68723 [😂, 😂, 😂, 😂] \n",
"68724 [😀] \n",
"68725 [😎] \n",
"68726 [✨] \n",
"68727 [🤷] \n",
"68728 [😘] \n",
"68729 [😂] \n",
"68730 [🙏] \n",
"68731 [💁] \n",
"68732 [🍃, 🌻, 🌻, 🍃, 🍃, 💐, 💐, 🍃, 🙋] \n",
"\n",
" HASHTAGS \\\n",
"0 [] \n",
"1 [] \n",
"2 [] \n",
"3 [] \n",
"4 [] \n",
"5 [] \n",
"6 [] \n",
"7 [] \n",
"8 [] \n",
"9 [] \n",
"10 [] \n",
"11 [] \n",
"12 [#mortdale, #partofthefamily, #gorgeousboy] \n",
"13 [] \n",
"14 [#7YearsOfKMH2] \n",
"15 [] \n",
"16 [] \n",
"17 [] \n",
"18 [] \n",
"19 [#footfetishnation] \n",
"20 [] \n",
"21 [] \n",
"22 [] \n",
"23 [] \n",
"24 [] \n",
"25 [] \n",
"26 [] \n",
"27 [] \n",
"28 [] \n",
"29 [] \n",
"... ... \n",
"68703 [#halloweencostumes] \n",
"68704 [] \n",
"68705 [#MUFC] \n",
"68706 [] \n",
"68707 [#BurkeCentre] \n",
"68708 [] \n",
"68709 [] \n",
"68710 [#portlandoregon, #portlandhalloween, #carrie…] \n",
"68711 [] \n",
"68712 [] \n",
"68713 [] \n",
"68714 [] \n",
"68715 [] \n",
"68716 [] \n",
"68717 [] \n",
"68718 [] \n",
"68719 [] \n",
"68720 [] \n",
"68721 [] \n",
"68722 [#WednesdayWisdom] \n",
"68723 [] \n",
"68724 [] \n",
"68725 [] \n",
"68726 [] \n",
"68727 [] \n",
"68728 [] \n",
"68729 [] \n",
"68730 [#NYCStrong] \n",
"68731 [#GreatSuccess] \n",
"68732 [] \n",
"\n",
" LINKED_USER datetime \\\n",
"0 [] 2017-11-01 13:29:00 \n",
"1 [] 2017-11-01 13:29:00 \n",
"2 [] 2017-11-01 13:29:03 \n",
"3 [] 2017-11-01 13:29:04 \n",
"4 [@hiphopphiIes] 2017-11-01 13:29:06 \n",
"5 [@WizMommma] 2017-11-01 13:29:02 \n",
"6 [] 2017-11-01 13:29:05 \n",
"7 [] 2017-11-01 13:29:07 \n",
"8 [@SeaDimon, @lsarsour] 2017-11-01 13:29:09 \n",
"9 [@Ian_khetye] 2017-11-01 13:29:10 \n",
"10 [] 2017-11-01 13:29:13 \n",
"11 [@Dimafadma] 2017-11-01 13:29:15 \n",
"12 [] 2017-11-01 13:29:16 \n",
"13 [] 2017-11-01 13:29:20 \n",
"14 [] 2017-11-01 13:29:20 \n",
"15 [] 2017-11-01 13:29:22 \n",
"16 [] 2017-11-01 13:29:23 \n",
"17 [@yungbabytate] 2017-11-01 13:29:23 \n",
"18 [@cmckenney] 2017-11-01 13:29:23 \n",
"19 [] 2017-11-01 13:29:25 \n",
"20 [] 2017-11-01 13:29:25 \n",
"21 [] 2017-11-01 13:29:26 \n",
"22 [] 2017-11-01 13:29:26 \n",
"23 [@ctrlpurp] 2017-11-01 13:29:29 \n",
"24 [@saunders_court1] 2017-11-01 13:29:30 \n",
"25 [] 2017-11-01 13:29:31 \n",
"26 [] 2017-11-01 13:29:32 \n",
"27 [@xxxtentacion] 2017-11-01 13:29:35 \n",
"28 [] 2017-11-01 13:29:35 \n",
"29 [@justinerooney_] 2017-11-01 13:29:37 \n",
"... ... ... \n",
"68703 [] 2017-11-01 07:23:04 \n",
"68704 [] 2017-11-01 07:23:05 \n",
"68705 [] 2017-11-01 07:23:06 \n",
"68706 [] 2017-11-01 07:23:07 \n",
"68707 [] 2017-11-01 07:23:18 \n",
"68708 [@mychosliaheart, @BarrettoJulia, @iamjoshuaga... 2017-11-01 07:23:18 \n",
"68709 [] 2017-11-01 07:23:24 \n",
"68710 [] 2017-11-01 07:23:24 \n",
"68711 [@8limbsbondi...] 2017-11-01 07:23:26 \n",
"68712 [] 2017-11-01 07:23:27 \n",
"68713 [@rfrandrea, @AdaaanAndyyy] 2017-11-01 07:23:30 \n",
"68714 [@peachshua1230] 2017-11-01 07:23:31 \n",
"68715 [] 2017-11-01 07:23:34 \n",
"68716 [] 2017-11-01 07:23:34 \n",
"68717 [@BeachBoy_Gab] 2017-11-01 07:23:34 \n",
"68718 [] 2017-11-01 07:23:34 \n",
"68719 [] 2017-11-01 07:23:35 \n",
"68720 [] 2017-11-01 07:23:36 \n",
"68721 [] 2017-11-01 07:23:37 \n",
"68722 [] 2017-11-01 07:23:41 \n",
"68723 [@Louis_Tomlinson, @NiallOfficial] 2017-11-01 07:23:44 \n",
"68724 [] 2017-11-01 07:23:45 \n",
"68725 [] 2017-11-01 07:23:45 \n",
"68726 [] 2017-11-01 07:23:46 \n",
"68727 [] 2017-11-01 07:23:46 \n",
"68728 [] 2017-11-01 07:23:46 \n",
"68729 [@discopiggu] 2017-11-01 07:23:46 \n",
"68730 [] 2017-11-01 07:23:50 \n",
"68731 [@BrianyH] 2017-11-01 07:23:50 \n",
"68732 [@amitbarman520] 2017-11-01 07:23:53 \n",
"\n",
" id lang person reply_to \\\n",
"0 925716304635547600 en 31507978 NaN \n",
"1 925716304664911900 en 231994649 NaN \n",
"2 925716317214089200 en 2592765104 NaN \n",
"3 925716321416949800 en 278737933 NaN \n",
"4 925716329801310200 en 824586253634981900 9.257162e+17 \n",
"5 925716313019965400 en 1581953814 9.257088e+17 \n",
"6 925716325607133200 en 1001999683 NaN \n",
"7 925716334008082400 en 745222369183043600 NaN \n",
"8 925716342401052700 en 798557155217539100 9.257147e+17 \n",
"9 925716346570240000 en 744396039126421500 9.250629e+17 \n",
"10 925716359182520300 en 721490010118205400 8.965900e+17 \n",
"11 925716367558545400 en 520536723 9.257159e+17 \n",
"12 925716371735900200 en 850852815941517300 NaN \n",
"13 925716388513230800 en 914145041588867100 NaN \n",
"14 925716388525645800 en 2425405622 NaN \n",
"15 925716396931240000 en 4614871873 NaN \n",
"16 925716401125331000 en 2831608345 NaN \n",
"17 925716401133948900 en 788571974633009200 9.255778e+17 \n",
"18 925716401125544000 en 218307802 9.257115e+17 \n",
"19 925716409489002500 en 885261166146179100 NaN \n",
"20 925716409497272300 en 831437760833609700 NaN \n",
"21 925716413699854300 en 231664542 NaN \n",
"22 925716413679009800 en 3196847035 NaN \n",
"23 925716426278735900 en 918492858352635900 9.257161e+17 \n",
"24 925716430473039900 en 3471187337 9.257163e+17 \n",
"25 925716434667184100 en 780060488600199200 NaN \n",
"26 925716438853345300 en 388380690 NaN \n",
"27 925716451457163300 en 899320696869974000 NaN \n",
"28 925716451461357600 en 784790670 NaN \n",
"29 925716459828936700 en 3051266655 NaN \n",
"... ... ... ... ... \n",
"68703 925624214522036200 en 1672876458 NaN \n",
"68704 925624218682777600 en 382473866 NaN \n",
"68705 925624222889766900 en 893145405457911800 NaN \n",
"68706 925624227088121900 en 240378516 NaN \n",
"68707 925624273237983200 en 784620573209002000 NaN \n",
"68708 925624273212805100 en 170998187 9.254136e+17 \n",
"68709 925624298395533300 en 1348667816 NaN \n",
"68710 925624298378801200 en 722481645765300200 NaN \n",
"68711 925624306779897900 en 2443251500 NaN \n",
"68712 925624310974136300 en 2406186390 NaN \n",
"68713 925624323557146600 en 1039448149 9.256150e+17 \n",
"68714 925624327755591700 en 845085544589672400 9.256009e+17 \n",
"68715 925624340342812700 en 924752524871131100 NaN \n",
"68716 925624340355280900 en 419493819 NaN \n",
"68717 925624340346937300 en 2457745952 9.256219e+17 \n",
"68718 925624340338507800 en 1955767531 NaN \n",
"68719 925624344524361700 en 796490344581898200 NaN \n",
"68720 925624348710285300 en 907808317124177900 NaN \n",
"68721 925624352929910800 en 262162415 NaN \n",
"68722 925624369715515400 en 574882525 NaN \n",
"68723 925624382269124600 en 556175173 9.254038e+17 \n",
"68724 925624386455031800 en 1610265588 NaN \n",
"68725 925624386454937600 en 4760724450 NaN \n",
"68726 925624390657572900 en 882858115636514800 NaN \n",
"68727 925624390682849300 en 188129628 NaN \n",
"68728 925624390666129400 en 2473135939 NaN \n",
"68729 925624390670106600 en 2373584209 9.256241e+17 \n",
"68730 925624407459971100 en 181689756 NaN \n",
"68731 925624407460057100 en 60160788 9.254610e+17 \n",
"68732 925624420022063100 en 3792290725 9.256215e+17 \n",
"\n",
" text \n",
"0 fashionbombdaily's photo <EMOJI><EMOJI>🏼 \n",
"1 It’s scary how on point my horoscope be <EMOJI... \n",
"2 Woooaaaahhh <EMOJI> \n",
"3 <EMOJI> vivalcli: Portraits by Zhao Guojing an... \n",
"4 <USER> i wanna know too<EMOJI><EMOJI> \n",
"5 <USER> veda was yoda too <EMOJI><EMOJI> \n",
"6 I’m less stressed about turning 30 now <EMOJI>... \n",
"7 Full charged. <EMOJI> \n",
"8 <USER> That’s part of the problem, (they) <USE... \n",
"9 <USER> got me emotional there<EMOJI><EMOJI><EM... \n",
"10 back to the yellow <EMOJI> \n",
"11 <USER> Happy month to you and your loved ones ... \n",
"12 Maxx and Patricia. Family hangs at For Good He... \n",
"13 I need to STOP beating myself up with my thoug... \n",
"14 Cutest Son <EMOJI>Roll no. 31 <EMOJI> <HASHTAG> \n",
"15 By the summer I should have everything up and ... \n",
"16 I know my English is not that good but that do... \n",
"17 <USER> I <EMOJI> u mama \n",
"18 <USER> That picture was NOT taken this morning... \n",
"19 Welcome to <HASHTAG> <EMOJI> \n",
"20 Awkward <EMOJI><EMOJI> \n",
"21 back at it with supernatural <EMOJI><EMOJI> \n",
"22 One of the best things I've learned was to sto... \n",
"23 <USER> Can I taste?<EMOJI><EMOJI><EMOJI> \n",
"24 <USER> we miss you ☹️<EMOJI> \n",
"25 Actually my bias in WJSN are Eunseo &amp; Bona... \n",
"26 I so cannot be bothered with the rest of the d... \n",
"27 <USER> 2lit4life<EMOJI> \n",
"28 I’m not stop saying that!<EMOJI><EMOJI> \n",
"29 <USER> HAPPY BIHDAY <EMOJI><EMOJI><EMOJI><EMOJ... \n",
"... ... \n",
"68703 <HASHTAG> this one falls under the weird crazy... \n",
"68704 I'm not allowed to have chocolates yet, then I... \n",
"68705 Manchester United manager Mourinho slams 'spec... \n",
"68706 <EMOJI> en Bushwhick \n",
"68707 : <HASHTAG> <EMOJI> <EMOJI><EMOJI> For Taxi <E... \n",
"68708 <USER> <USER> <USER> Look, Mammeh and Daddeh! ... \n",
"68709 Life is so good with you <EMOJI> \n",
"68710 Happy Halloween! <EMOJI>🏽<EMOJI><EMOJI> <HASHT... \n",
"68711 Some work on the ropes in today’s boxing class... \n",
"68712 Scotty and Kristen’s halloween costumes <EMOJI> \n",
"68713 <USER> <USER> May pre-month celebration sis <E... \n",
"68714 <USER> Awww <EMOJI> Ajsksjdjd im smiling like ... \n",
"68715 I hate when I send a text or snap n I'm so anx... \n",
"68716 The answer is no I have no plans and I never l... \n",
"68717 <USER> LMAOOO I'm so proud <EMOJI> \n",
"68718 My cousin/little sister is leaving to San Fran... \n",
"68719 Can't be alone w my thoughts tonight so just g... \n",
"68720 <EMOJI><EMOJI><EMOJI><EMOJI><EMOJI> ambot!!! \n",
"68721 <EMOJI>🏽<EMOJI>🏽 1st of the month!!Happy 1st o... \n",
"68722 <HASHTAG> ... stay in bed <EMOJI> \n",
"68723 <USER> <USER> THIS IS GOLD. GOLD. <EMOJI><EMOJ... \n",
"68724 <EMOJI> thank you for the kind compliment \n",
"68725 Enjoyed the silence <EMOJI> \n",
"68726 OS: Spiderman Homecoming <EMOJI> \n",
"68727 Go to hell <EMOJI>🏽‍♀️ \n",
"68728 Thank you Yomi! <EMOJI> \n",
"68729 <USER> Lol. Just enjoy the stars. Music Kidhar... \n",
"68730 Thoughts and prayers for NY<EMOJI>🏻 <HASHTAG> \n",
"68731 <USER> I searched COCK, PENIS, SHLONG, WINKY, ... \n",
"68732 <USER> Thank you so much<EMOJI><EMOJI><EMOJI><... \n",
"\n",
"[68733 rows x 9 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"twitter_data = pd.read_json(json_files[0], encoding=\"utf-8\")\n",
"twitter_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* extracting emojis and text"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"emojis = twitter_data['EMOJI']\n",
"plain_text = twitter_data['text']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* make our plain text more \"plain\":\n",
" * removing the keyword `<EMOJI>` (just for the beginning)\n",
" * removing remaining useless emojis, like skin modifier etc."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# defining blacklist for modifier emojis:\n",
"emoji_blacklist = set([\n",
" chr(0x1F3FB),\n",
" chr(0x1F3FC),\n",
" chr(0x1F3FD),\n",
" chr(0x1F3FE),\n",
" chr(0x1F3FF),\n",
" chr(0x2642),\n",
" chr(0x2640)\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# filtering them and the EMOJI keyword out:\n",
"plain_text = plain_text.str.replace(\"(<EMOJI>|<USER>|<HASHTAG>)\",\"\").str.replace(\"[\" + \"\".join(list(emoji_blacklist)) + \"]\",\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* convert all emojis to a sentiment vector"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"labels = emoji2sent([latest(e) for e in emojis])\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"68733"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(labels)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"wrong_labels = np.isnan(np.linalg.norm(labels, axis=1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* remove all data we have no label for"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"labels = labels[np.invert(wrong_labels)]\n",
"plain_text = plain_text[np.invert(wrong_labels)]\n",
"emojis = emojis[np.invert(wrong_labels)]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"33368 33368 33368\n"
]
}
],
"source": [
"print(len(labels), len(emojis), len(plain_text))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Apply stemming and lemmatization (if needed)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"from nltk.stem.snowball import SnowballStemmer\n",
"from nltk.stem import WordNetLemmatizer\n",
"from nltk import pos_tag\n",
"from nltk import word_tokenize\n",
"from nltk.corpus import wordnet"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def get_wordnet_pos(treebank_tag):\n",
"\n",
" if treebank_tag.startswith('J'):\n",
" return wordnet.ADJ\n",
" elif treebank_tag.startswith('V'):\n",
" return wordnet.VERB\n",
" elif treebank_tag.startswith('N'):\n",
" return wordnet.NOUN\n",
" elif treebank_tag.startswith('R'):\n",
" return wordnet.ADV\n",
" else:\n",
" return wordnet.NOUN"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"stemmer = SnowballStemmer(\"english\")\n",
"for key in plain_text.keys():\n",
" stemmed_sent = []\n",
" for word in plain_text[key].split(\" \"):\n",
" word_stemmed = stemmer.stem(word)\n",
" stemmed_sent.append(word_stemmed)\n",
" stemmed_sent = (\" \").join(stemmed_sent)\n",
" plain_text[key] = stemmed_sent"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 woooaaaahhh\n",
"4 i wan na know too\n",
"6 i 'm le stress about turn 30 now i think i'v r...\n",
"9 got me emot there\n",
"14 cutest son roll no . 31\n",
"15 by the summer i should have everyth up and run...\n",
"18 that pictur wa not taken this morning !\n",
"26 i so can not be bother with the rest of the da...\n",
"27 2lit4lif\n",
"35 hate fall asleep befor i put my phone on the c...\n",
"36 unexpect saw two of my crush today . this day ...\n",
"40 elvi whi o whi ? our girl wa such a love stori...\n",
"42 you'r late i ate them all\n",
"43 me toooo\n",
"47 the pressur is just too much\n",
"51 i broke grammar\n",
"52 have not desir to go to work today\n",
"53 omg do n't it scari all i know is that i do no...\n",
"56 achoo mr. fuck nigga you , you done caught cau...\n",
"58 i can never catch a dang break !\n",
"59 pas my p on two hour of sleep\n",
"60 i 'm realli not amus\n",
"65 i can help you\n",
"71 whew i slept good af last night\n",
"74 this would be epic . pizza and play perfect gi...\n",
"76 hey , it 1st novemb\n",
"80 u is to press bitch for me to have been speak ...\n",
"88 lmfao thought it wa just me be bitter\n",
"89 yupp yuppp . super prettttyyy , my heart cant ...\n",
"90 bakit halo halong seri binanggit mo be ? none ...\n",
" ... \n",
"68675 go back to dark hair tomorrow , mhmm yasss\n",
"68677 i miss them so much\n",
"68678 i wan na feel your gut too\n",
"68683 everi time\n",
"68687 i neither own nor watch tv . now go watch cnn\n",
"68688 revolutionari love\n",
"68694 ear worm is run in the famili after sing an aw...\n",
"68696 ill never look at you the same . yeah you got ...\n",
"68699 it our 3 year anniversari today to celebrate ,...\n",
"68700 person that scare me\n",
"68701 damn girl . can u look ani hotter than this ? ...\n",
"68703 this one fall under the weird crazi one .\n",
"68704 i 'm not allow to have chocol yet , then i uni...\n",
"68705 manchest unit manag mourinho slam specialists'...\n",
"68708 look , mammeh and daddeh ! cuuutee..\n",
"68709 life is so good with you\n",
"68710 happi halloween !\n",
"68712 scotti and kristen halloween costum\n",
"68713 may pre-month celebr si\n",
"68717 lmaooo i 'm so proud\n",
"68720 ambot ! ! !\n",
"68721 1st of the month ! ! happi 1st of novemb *53 d...\n",
"68722 ... stay in bed\n",
"68723 this is gold . gold .\n",
"68724 thank you for the kind compliment\n",
"68725 enjoy the silenc\n",
"68728 thank you yomi !\n",
"68729 lol . just enjoy the star . music kidhar aur b...\n",
"68730 thought and prayer for ny\n",
"68732 thank you so muchhav a happi wednesday and a g...\n",
"Name: text, Length: 33368, dtype: object\n"
]
}
],
"source": [
"lemmatizer = WordNetLemmatizer()\n",
"for key in plain_text.keys():\n",
" lemmatized_sent = []\n",
" sent_pos = pos_tag(word_tokenize(plain_text[key]))\n",
" for word in sent_pos:\n",
" wordnet_pos = get_wordnet_pos(word[1].lower())\n",
" word_lemmatized = lemmatizer.lemmatize(word[0], pos=wordnet_pos)\n",
" lemmatized_sent.append(word_lemmatized)\n",
" lemmatized_sent = (\" \").join(lemmatized_sent)\n",
" plain_text[key] = lemmatized_sent\n",
"print(plain_text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* generate weights:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# at first count over our table\n",
"emoji_count = {}\n",
"\n",
"\n",
"for e_list in emojis:\n",
" for e in set(e_list):\n",
" if e not in emoji_count:\n",
" emoji_count[e] = 0\n",
" emoji_count[e] += 1\n",
"\n",
"emoji_count\n",
"emoji_sum = sum([emoji_count[e] for e in emoji_count])\n",
"\n",
"emoji_weights = {}\n",
"for e in emoji_count:\n",
" # tfidf for emojis\n",
" emoji_weights[e] = np.log((emoji_sum / emoji_count[e]))\n",
"\n",
"weights_sum= sum([emoji_weights[x] for x in emoji_weights])\n",
" \n",
"# normalize:\n",
"for e in emoji_weights:\n",
" emoji_weights[e] = emoji_weights[e] / weights_sum\n",
"\n",
"emoji_weights['X'] = 0 # dummy values\n",
"emoji_count['X'] = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* most used emojis in Dataset"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import operator"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('😂', 10182),\n",
" ('😭', 3893),\n",
" ('😍', 2866),\n",
" ('😩', 1647),\n",
" ('😊', 1450),\n",
" ('😘', 1151),\n",
" ('🙏', 1089),\n",
" ('🙌', 1003),\n",
" ('😉', 752),\n",
" ('😁', 697),\n",
" ('😅', 651),\n",
" ('😎', 606),\n",
" ('😢', 544),\n",
" ('😒', 539),\n",
" ('😏', 478),\n",
" ('😌', 434),\n",
" ('😔', 415),\n",
" ('😋', 397),\n",
" ('😀', 392),\n",
" ('😤', 368)]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"['😂',\n",
" '😭',\n",
" '😍',\n",
" '😩',\n",
" '😊',\n",
" '😘',\n",
" '🙏',\n",
" '🙌',\n",
" '😉',\n",
" '😁',\n",
" '😅',\n",
" '😎',\n",
" '😢',\n",
" '😒',\n",
" '😏',\n",
" '😌',\n",
" '😔',\n",
" '😋',\n",
" '😀',\n",
" '😤']"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sorted_emoji_count = list(reversed(sorted(emoji_count.items(), key=operator.itemgetter(1))))\n",
"display(sorted_emoji_count[:20])\n",
"\n",
"top_emojis = [x[0] for x in sorted_emoji_count[:20]]\n",
"display(top_emojis)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* only learn the most used ones:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"26197 26197 26197\n"
]
}
],
"source": [
"in_top = [sentiment_vector_to_emoji(x) in top_emojis for x in labels]\n",
"labels = labels[in_top]\n",
"plain_text = plain_text[in_top]\n",
"emojis = emojis[in_top]\n",
"print(len(labels), len(emojis), len(plain_text))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* generating train and test set:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"X1, Xt1, y1, yt1 = train_test_split(plain_text, labels, test_size=0.1, random_state=4222)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"#y1_weights = np.array([(sum([emoji_weights[e] for e in e_list]) / len(e_list)) if len(e_list) > 0 else 0 for e_list in sent2emoji(y1)])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"vectorizer = TfidfVectorizer(stop_words='english')\n",
"vec_train = vectorizer.fit_transform(X1)\n",
"vec_test = vectorizer.transform(Xt1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* train. this can take a very long time..."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from sklearn.neural_network import MLPClassifier as MLP\n",
"from sklearn.multiclass import OneVsRestClassifier as OVRC\n",
"from sklearn.tree import DecisionTreeClassifier as DTC\n",
"\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"def train(max_size = 10000, layers=[(1024, 'relu'),(y1[0].shape[0],'softmax')], random_state=4222, ovrc=False, n_iter=5):\n",
" \n",
" model = Sequential()\n",
" \n",
" # build mlp layers:\n",
" keras_layers = []\n",
" first_layer = True\n",
" for layer in layers:\n",
" if first_layer:\n",
" model.add(Dense(units=layer[0], activation=layer[1], input_dim=vectorizer.transform([\" \"])[0]._shape[1]))\n",
" first_layer = False\n",
" else:\n",
" model.add(Dense(units=layer[0], activation=layer[1]))\n",
" \n",
" #mlp = MLPClassifier(layers=sknn_layers, random_state=random_state, verbose=True, n_iter=n_iter, batch_size=100)\n",
" \n",
" model.compile(loss='mean_squared_error',\n",
" optimizer='adam')\n",
" \n",
" clf = OVRC(model) if ovrc else model\n",
"\n",
" clf.fit(vec_train[:max_size].A, y1[:max_size], validation_split=0.2, epochs=n_iter)#, sample_weight=y1_weights[:max_size])\n",
" \n",
" return clf"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 18861 samples, validate on 4716 samples\n",
"Epoch 1/3\n",
"18861/18861 [==============================] - 1106s 59ms/step - loss: 0.0185 - val_loss: 0.0152\n",
"Epoch 2/3\n",
"18861/18861 [==============================] - 1104s 59ms/step - loss: 0.0107 - val_loss: 0.0163\n",
"Epoch 3/3\n",
"18861/18861 [==============================] - 1106s 59ms/step - loss: 0.0065 - val_loss: 0.0166\n"
]
}
],
"source": [
"clf = train(max_size=100000,layers=[(10000, 'relu'),(5000, 'relu'),(2500, 'relu'),(y1[0].shape[0],None)], n_iter=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* make a prediction and store it in a csv file:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"pred = clf.predict(vectorizer.transform(Xt1))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.011668838\n",
"0.03387511671001757\n"
]
}
],
"source": [
"print(np.linalg.norm(np.var(pred, axis=0)))\n",
"print(np.linalg.norm(np.var(labels, axis=0)))\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# build a dataframe to visualize test results:\n",
"testlist = pd.DataFrame({'text': Xt1, \n",
" 'teacher': sent2emoji(yt1),\n",
" 'teacher_sentiment': yt1.tolist(),\n",
" 'predict': sent2emoji(pred, custom_target_emojis=top_emojis),\n",
" 'predicted_sentiment': pred.tolist()})"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>teacher</th>\n",
" <th>teacher_sentiment</th>\n",
" <th>predict</th>\n",
" <th>predicted_sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>35671</th>\n",
" <td>i feel like i care so much more in everi situat</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.4447824954986572, 0.30056363344192505, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25683</th>\n",
" <td>i did not meat to add that 2 there ... hav see...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😁</td>\n",
" <td>[0.5660845637321472, 0.1737498641014099, 0.284...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8985</th>\n",
" <td>never…</td>\n",
" <td>😊</td>\n",
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
" <td>😂</td>\n",
" <td>[0.4871470034122467, 0.26607102155685425, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5410</th>\n",
" <td>lmao on me ! ! ! wtf wa he suppos to say</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😢</td>\n",
" <td>[0.4061833620071411, 0.3226468861103058, 0.273...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62611</th>\n",
" <td>this dude alway help me get through my school ...</td>\n",
" <td>😊</td>\n",
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
" <td>😂</td>\n",
" <td>[0.4549962878227234, 0.21886931359767914, 0.33...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48197</th>\n",
" <td>happi b'day sir</td>\n",
" <td>😊</td>\n",
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
" <td>😀</td>\n",
" <td>[0.6561306715011597, 0.11821962147951126, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23654</th>\n",
" <td>you need some good old fashion swedish jesus</td>\n",
" <td>🙏</td>\n",
" <td>[0.4983755685510071, 0.08057179987004548, 0.42...</td>\n",
" <td>😅</td>\n",
" <td>[0.4600130021572113, 0.28595462441444397, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58207</th>\n",
" <td>these late shift are make me not have a social...</td>\n",
" <td>😅</td>\n",
" <td>[0.47186147186147187, 0.2922077922077922, 0.23...</td>\n",
" <td>😂</td>\n",
" <td>[0.49543458223342896, 0.25571855902671814, 0.2...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td>dc this weekend</td>\n",
" <td>😍</td>\n",
" <td>[0.7296744771190439, 0.05173769460607014, 0.21...</td>\n",
" <td>😌</td>\n",
" <td>[0.6040589213371277, 0.15823380649089813, 0.26...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26310</th>\n",
" <td>paul lad you 'll make e blush</td>\n",
" <td>😊</td>\n",
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
" <td>😂</td>\n",
" <td>[0.452500581741333, 0.2882971167564392, 0.2790...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30892</th>\n",
" <td>did you have a fun halloween ?</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😂</td>\n",
" <td>[0.4644194543361664, 0.2708289325237274, 0.277...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11868</th>\n",
" <td>hi handsom</td>\n",
" <td>😍</td>\n",
" <td>[0.7296744771190439, 0.05173769460607014, 0.21...</td>\n",
" <td>😊</td>\n",
" <td>[0.7254493236541748, 0.12355809658765793, 0.22...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46219</th>\n",
" <td>i 'm not okay with this , i 'm su snapchat</td>\n",
" <td>😭</td>\n",
" <td>[0.34310532030401736, 0.4364820846905538, 0.22...</td>\n",
" <td>😔</td>\n",
" <td>[0.3264158070087433, 0.48023173213005066, 0.23...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13583</th>\n",
" <td>my parent be so mad i be buy new stuff &amp; amp ;...</td>\n",
" <td>😭</td>\n",
" <td>[0.34310532030401736, 0.4364820846905538, 0.22...</td>\n",
" <td>😂</td>\n",
" <td>[0.4271591007709503, 0.29361462593078613, 0.29...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43843</th>\n",
" <td>one of the few song that calm me down esp on f...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😂</td>\n",
" <td>[0.44168680906295776, 0.2790682315826416, 0.29...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63589</th>\n",
" <td>iphon x bouta be the last phone we ever buy</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.41863512992858887, 0.3106093108654022, 0.28...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53695</th>\n",
" <td>visit my main man today ❤ i miss u papa</td>\n",
" <td>😭</td>\n",
" <td>[0.34310532030401736, 0.4364820846905538, 0.22...</td>\n",
" <td>😁</td>\n",
" <td>[0.5650997757911682, 0.19236208498477936, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67529</th>\n",
" <td>donut</td>\n",
" <td>😍</td>\n",
" <td>[0.7296744771190439, 0.05173769460607014, 0.21...</td>\n",
" <td>😂</td>\n",
" <td>[0.45511549711227417, 0.28582143783569336, 0.2...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25493</th>\n",
" <td>ha anyon heard this by ? who the fuck knew he ...</td>\n",
" <td>😘</td>\n",
" <td>[0.7546600877192983, 0.05290570175438596, 0.19...</td>\n",
" <td>😅</td>\n",
" <td>[0.4276219606399536, 0.30413898825645447, 0.28...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19486</th>\n",
" <td>wow superrbb</td>\n",
" <td>😍</td>\n",
" <td>[0.7296744771190439, 0.05173769460607014, 0.21...</td>\n",
" <td>😊</td>\n",
" <td>[0.7149834036827087, 0.10459273308515549, 0.24...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48449</th>\n",
" <td>of cours they do n't . their perfect model of ...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😂</td>\n",
" <td>[0.5363025665283203, 0.22163532674312592, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4504</th>\n",
" <td>plea pick me . pick me . pick me . please .</td>\n",
" <td>😍</td>\n",
" <td>[0.7296744771190439, 0.05173769460607014, 0.21...</td>\n",
" <td>😂</td>\n",
" <td>[0.4641677737236023, 0.18824045360088348, 0.37...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40285</th>\n",
" <td>shiid no crack is wack</td>\n",
" <td>😭</td>\n",
" <td>[0.34310532030401736, 0.4364820846905538, 0.22...</td>\n",
" <td>😂</td>\n",
" <td>[0.44292521476745605, 0.28201037645339966, 0.2...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56741</th>\n",
" <td>pj still sleep like a newborn</td>\n",
" <td>😩</td>\n",
" <td>[0.22289823008849557, 0.5912610619469026, 0.18...</td>\n",
" <td>😢</td>\n",
" <td>[0.40168094635009766, 0.3777309060096741, 0.24...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22948</th>\n",
" <td>an opinion doe n't mean you make sen first off...</td>\n",
" <td>😌</td>\n",
" <td>[0.6240601503759399, 0.13984962406015036, 0.23...</td>\n",
" <td>😂</td>\n",
" <td>[0.4365620017051697, 0.2830066680908203, 0.294...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68426</th>\n",
" <td>missyou too</td>\n",
" <td>😘</td>\n",
" <td>[0.7546600877192983, 0.05290570175438596, 0.19...</td>\n",
" <td>😭</td>\n",
" <td>[0.3452186584472656, 0.4593580365180969, 0.223...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13431</th>\n",
" <td>i swear she did</td>\n",
" <td>😩</td>\n",
" <td>[0.22289823008849557, 0.5912610619469026, 0.18...</td>\n",
" <td>😂</td>\n",
" <td>[0.4436468482017517, 0.2736954987049103, 0.294...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66287</th>\n",
" <td>it true , he wa the mutt ( big-d ) nut .</td>\n",
" <td>😉</td>\n",
" <td>[0.5634451019066403, 0.0992767915844839, 0.337...</td>\n",
" <td>😅</td>\n",
" <td>[0.47334975004196167, 0.2881445586681366, 0.26...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41980</th>\n",
" <td>is happen so happi</td>\n",
" <td>😭</td>\n",
" <td>[0.34310532030401736, 0.4364820846905538, 0.22...</td>\n",
" <td>😅</td>\n",
" <td>[0.4635038673877716, 0.30251604318618774, 0.26...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34632</th>\n",
" <td>saw that the first one said sose you tmmrw and np</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.43297499418258667, 0.31000325083732605, 0.2...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47218</th>\n",
" <td>keep it i do n't want it</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.470976859331131, 0.2997904419898987, 0.2489...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35087</th>\n",
" <td>6 year ago today we said our final good bye , ...</td>\n",
" <td>😢</td>\n",
" <td>[0.39118825100133514, 0.38451268357810414, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.4478594660758972, 0.30181050300598145, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61252</th>\n",
" <td>amen</td>\n",
" <td>🙏</td>\n",
" <td>[0.4983755685510071, 0.08057179987004548, 0.42...</td>\n",
" <td>😁</td>\n",
" <td>[0.5783949494361877, 0.15405726432800293, 0.30...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39960</th>\n",
" <td>fact</td>\n",
" <td>😎</td>\n",
" <td>[0.5981432360742706, 0.10477453580901856, 0.29...</td>\n",
" <td>😂</td>\n",
" <td>[0.4938848316669464, 0.24237176775932312, 0.25...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>365</th>\n",
" <td>nw final found someon hi loydi</td>\n",
" <td>😅</td>\n",
" <td>[0.47186147186147187, 0.2922077922077922, 0.23...</td>\n",
" <td>😌</td>\n",
" <td>[0.6203110218048096, 0.17863908410072327, 0.23...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50665</th>\n",
" <td>i need ur shoulder to cri on</td>\n",
" <td>😢</td>\n",
" <td>[0.39118825100133514, 0.38451268357810414, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.4619033932685852, 0.2977892756462097, 0.267...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21007</th>\n",
" <td>awww you 'll get me teari eye gurl !</td>\n",
" <td>😘</td>\n",
" <td>[0.7546600877192983, 0.05290570175438596, 0.19...</td>\n",
" <td>😅</td>\n",
" <td>[0.5029815435409546, 0.2711288630962372, 0.253...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18819</th>\n",
" <td>〰happi hump day to all my ladi is def sweet like</td>\n",
" <td>😘</td>\n",
" <td>[0.7546600877192983, 0.05290570175438596, 0.19...</td>\n",
" <td>🙌</td>\n",
" <td>[0.6974080801010132, 0.11416944861412048, 0.24...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42421</th>\n",
" <td>thanks .</td>\n",
" <td>😊</td>\n",
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
" <td>😁</td>\n",
" <td>[0.5765037536621094, 0.18341206014156342, 0.26...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38705</th>\n",
" <td>it okay i did n't see you until i wa in your face</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😢</td>\n",
" <td>[0.4030645787715912, 0.36543628573417664, 0.25...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47033</th>\n",
" <td>fall in with alexissdang ⬅️</td>\n",
" <td>😍</td>\n",
" <td>[0.7296744771190439, 0.05173769460607014, 0.21...</td>\n",
" <td>😌</td>\n",
" <td>[0.6219494342803955, 0.1795988380908966, 0.226...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38742</th>\n",
" <td>let your wild side free girl ! ! ! ! !</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😢</td>\n",
" <td>[0.4049544930458069, 0.35102248191833496, 0.25...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67610</th>\n",
" <td>can we get season 3 already ! ?</td>\n",
" <td>😭</td>\n",
" <td>[0.34310532030401736, 0.4364820846905538, 0.22...</td>\n",
" <td>😢</td>\n",
" <td>[0.416861355304718, 0.35468244552612305, 0.245...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61021</th>\n",
" <td>have to quot this bih rememb this nigga</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😂</td>\n",
" <td>[0.4401417672634125, 0.2810788154602051, 0.292...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14904</th>\n",
" <td>he so damn</td>\n",
" <td>😩</td>\n",
" <td>[0.22289823008849557, 0.5912610619469026, 0.18...</td>\n",
" <td>😂</td>\n",
" <td>[0.5411785244941711, 0.2148503214120865, 0.246...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50994</th>\n",
" <td>yasss it time for a great show maxloyal™♛ : gi...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>🙌</td>\n",
" <td>[0.7073097229003906, 0.12480126321315765, 0.23...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6973</th>\n",
" <td>got to love a fish finger sarni</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😌</td>\n",
" <td>[0.6375303864479065, 0.14495858550071716, 0.24...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3269</th>\n",
" <td>say dat</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😂</td>\n",
" <td>[0.5052645802497864, 0.21280284225940704, 0.30...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54827</th>\n",
" <td>if ur happi i 'm happi</td>\n",
" <td>😊</td>\n",
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.5138391852378845, 0.26520460844039917, 0.25...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19409</th>\n",
" <td>rockstar</td>\n",
" <td>😀</td>\n",
" <td>[0.6560364464692483, 0.08428246013667426, 0.25...</td>\n",
" <td>😂</td>\n",
" <td>[0.520979106426239, 0.22392335534095764, 0.265...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38703</th>\n",
" <td>god fuck me i alreadi fix it onc</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😔</td>\n",
" <td>[0.32009249925613403, 0.4819949269294739, 0.22...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25133</th>\n",
" <td>it social accept to listen to ani christma mus...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😁</td>\n",
" <td>[0.5703040361404419, 0.17875489592552185, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15339</th>\n",
" <td>i thrash p in imessag now he wan na bet on 2k</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.4571400284767151, 0.2949279546737671, 0.269...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28082</th>\n",
" <td>my fatass need some lemon pepper wing w a lil ...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😭</td>\n",
" <td>[0.36291953921318054, 0.4310339391231537, 0.22...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44462</th>\n",
" <td>fck off tristan yes soph collect u in 15 n we ...</td>\n",
" <td>😤</td>\n",
" <td>[0.2691131498470948, 0.4801223241590214, 0.250...</td>\n",
" <td>😌</td>\n",
" <td>[0.6122021675109863, 0.15579015016555786, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60212</th>\n",
" <td>open the bag</td>\n",
" <td>😋</td>\n",
" <td>[0.6784741144414169, 0.04495912806539509, 0.27...</td>\n",
" <td>😂</td>\n",
" <td>[0.470508873462677, 0.2711063623428345, 0.2810...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34950</th>\n",
" <td>i ask peopl to guess my zodiac sign and this o...</td>\n",
" <td>😤</td>\n",
" <td>[0.2691131498470948, 0.4801223241590214, 0.250...</td>\n",
" <td>😂</td>\n",
" <td>[0.4431285262107849, 0.27477312088012695, 0.29...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59462</th>\n",
" <td>i regret this sm</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😢</td>\n",
" <td>[0.40375131368637085, 0.3289208710193634, 0.27...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19465</th>\n",
" <td>fuck colleg</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.4494284391403198, 0.31076472997665405, 0.26...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8135</th>\n",
" <td>aye this wa my first time actual see it wendi ...</td>\n",
" <td>😂</td>\n",
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
" <td>😅</td>\n",
" <td>[0.42708122730255127, 0.31392431259155273, 0.2...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2620 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" text teacher \\\n",
"35671 i feel like i care so much more in everi situat 😂 \n",
"25683 i did not meat to add that 2 there ... hav see... 😂 \n",
"8985 never… 😊 \n",
"5410 lmao on me ! ! ! wtf wa he suppos to say 😂 \n",
"62611 this dude alway help me get through my school ... 😊 \n",
"48197 happi b'day sir 😊 \n",
"23654 you need some good old fashion swedish jesus 🙏 \n",
"58207 these late shift are make me not have a social... 😅 \n",
"374 dc this weekend 😍 \n",
"26310 paul lad you 'll make e blush 😊 \n",
"30892 did you have a fun halloween ? 😂 \n",
"11868 hi handsom 😍 \n",
"46219 i 'm not okay with this , i 'm su snapchat 😭 \n",
"13583 my parent be so mad i be buy new stuff & amp ;... 😭 \n",
"43843 one of the few song that calm me down esp on f... 😂 \n",
"63589 iphon x bouta be the last phone we ever buy 😂 \n",
"53695 visit my main man today ❤ i miss u papa 😭 \n",
"67529 donut 😍 \n",
"25493 ha anyon heard this by ? who the fuck knew he ... 😘 \n",
"19486 wow superrbb 😍 \n",
"48449 of cours they do n't . their perfect model of ... 😂 \n",
"4504 plea pick me . pick me . pick me . please . 😍 \n",
"40285 shiid no crack is wack 😭 \n",
"56741 pj still sleep like a newborn 😩 \n",
"22948 an opinion doe n't mean you make sen first off... 😌 \n",
"68426 missyou too 😘 \n",
"13431 i swear she did 😩 \n",
"66287 it true , he wa the mutt ( big-d ) nut . 😉 \n",
"41980 is happen so happi 😭 \n",
"34632 saw that the first one said sose you tmmrw and np 😂 \n",
"... ... ... \n",
"47218 keep it i do n't want it 😂 \n",
"35087 6 year ago today we said our final good bye , ... 😢 \n",
"61252 amen 🙏 \n",
"39960 fact 😎 \n",
"365 nw final found someon hi loydi 😅 \n",
"50665 i need ur shoulder to cri on 😢 \n",
"21007 awww you 'll get me teari eye gurl ! 😘 \n",
"18819 〰happi hump day to all my ladi is def sweet like 😘 \n",
"42421 thanks . 😊 \n",
"38705 it okay i did n't see you until i wa in your face 😂 \n",
"47033 fall in with alexissdang ⬅️ 😍 \n",
"38742 let your wild side free girl ! ! ! ! ! 😂 \n",
"67610 can we get season 3 already ! ? 😭 \n",
"61021 have to quot this bih rememb this nigga 😂 \n",
"14904 he so damn 😩 \n",
"50994 yasss it time for a great show maxloyal™♛ : gi... 😂 \n",
"6973 got to love a fish finger sarni 😂 \n",
"3269 say dat 😂 \n",
"54827 if ur happi i 'm happi 😊 \n",
"19409 rockstar 😀 \n",
"38703 god fuck me i alreadi fix it onc 😂 \n",
"25133 it social accept to listen to ani christma mus... 😂 \n",
"15339 i thrash p in imessag now he wan na bet on 2k 😂 \n",
"28082 my fatass need some lemon pepper wing w a lil ... 😂 \n",
"44462 fck off tristan yes soph collect u in 15 n we ... 😤 \n",
"60212 open the bag 😋 \n",
"34950 i ask peopl to guess my zodiac sign and this o... 😤 \n",
"59462 i regret this sm 😂 \n",
"19465 fuck colleg 😂 \n",
"8135 aye this wa my first time actual see it wendi ... 😂 \n",
"\n",
" teacher_sentiment predict \\\n",
"35671 [0.46813021474490496, 0.24716181096977158, 0.2... 😅 \n",
"25683 [0.46813021474490496, 0.24716181096977158, 0.2... 😁 \n",
"8985 [0.7040175768989329, 0.059322033898305086, 0.2... 😂 \n",
"5410 [0.46813021474490496, 0.24716181096977158, 0.2... 😢 \n",
"62611 [0.7040175768989329, 0.059322033898305086, 0.2... 😂 \n",
"48197 [0.7040175768989329, 0.059322033898305086, 0.2... 😀 \n",
"23654 [0.4983755685510071, 0.08057179987004548, 0.42... 😅 \n",
"58207 [0.47186147186147187, 0.2922077922077922, 0.23... 😂 \n",
"374 [0.7296744771190439, 0.05173769460607014, 0.21... 😌 \n",
"26310 [0.7040175768989329, 0.059322033898305086, 0.2... 😂 \n",
"30892 [0.46813021474490496, 0.24716181096977158, 0.2... 😂 \n",
"11868 [0.7296744771190439, 0.05173769460607014, 0.21... 😊 \n",
"46219 [0.34310532030401736, 0.4364820846905538, 0.22... 😔 \n",
"13583 [0.34310532030401736, 0.4364820846905538, 0.22... 😂 \n",
"43843 [0.46813021474490496, 0.24716181096977158, 0.2... 😂 \n",
"63589 [0.46813021474490496, 0.24716181096977158, 0.2... 😅 \n",
"53695 [0.34310532030401736, 0.4364820846905538, 0.22... 😁 \n",
"67529 [0.7296744771190439, 0.05173769460607014, 0.21... 😂 \n",
"25493 [0.7546600877192983, 0.05290570175438596, 0.19... 😅 \n",
"19486 [0.7296744771190439, 0.05173769460607014, 0.21... 😊 \n",
"48449 [0.46813021474490496, 0.24716181096977158, 0.2... 😂 \n",
"4504 [0.7296744771190439, 0.05173769460607014, 0.21... 😂 \n",
"40285 [0.34310532030401736, 0.4364820846905538, 0.22... 😂 \n",
"56741 [0.22289823008849557, 0.5912610619469026, 0.18... 😢 \n",
"22948 [0.6240601503759399, 0.13984962406015036, 0.23... 😂 \n",
"68426 [0.7546600877192983, 0.05290570175438596, 0.19... 😭 \n",
"13431 [0.22289823008849557, 0.5912610619469026, 0.18... 😂 \n",
"66287 [0.5634451019066403, 0.0992767915844839, 0.337... 😅 \n",
"41980 [0.34310532030401736, 0.4364820846905538, 0.22... 😅 \n",
"34632 [0.46813021474490496, 0.24716181096977158, 0.2... 😅 \n",
"... ... ... \n",
"47218 [0.46813021474490496, 0.24716181096977158, 0.2... 😅 \n",
"35087 [0.39118825100133514, 0.38451268357810414, 0.2... 😅 \n",
"61252 [0.4983755685510071, 0.08057179987004548, 0.42... 😁 \n",
"39960 [0.5981432360742706, 0.10477453580901856, 0.29... 😂 \n",
"365 [0.47186147186147187, 0.2922077922077922, 0.23... 😌 \n",
"50665 [0.39118825100133514, 0.38451268357810414, 0.2... 😅 \n",
"21007 [0.7546600877192983, 0.05290570175438596, 0.19... 😅 \n",
"18819 [0.7546600877192983, 0.05290570175438596, 0.19... 🙌 \n",
"42421 [0.7040175768989329, 0.059322033898305086, 0.2... 😁 \n",
"38705 [0.46813021474490496, 0.24716181096977158, 0.2... 😢 \n",
"47033 [0.7296744771190439, 0.05173769460607014, 0.21... 😌 \n",
"38742 [0.46813021474490496, 0.24716181096977158, 0.2... 😢 \n",
"67610 [0.34310532030401736, 0.4364820846905538, 0.22... 😢 \n",
"61021 [0.46813021474490496, 0.24716181096977158, 0.2... 😂 \n",
"14904 [0.22289823008849557, 0.5912610619469026, 0.18... 😂 \n",
"50994 [0.46813021474490496, 0.24716181096977158, 0.2... 🙌 \n",
"6973 [0.46813021474490496, 0.24716181096977158, 0.2... 😌 \n",
"3269 [0.46813021474490496, 0.24716181096977158, 0.2... 😂 \n",
"54827 [0.7040175768989329, 0.059322033898305086, 0.2... 😅 \n",
"19409 [0.6560364464692483, 0.08428246013667426, 0.25... 😂 \n",
"38703 [0.46813021474490496, 0.24716181096977158, 0.2... 😔 \n",
"25133 [0.46813021474490496, 0.24716181096977158, 0.2... 😁 \n",
"15339 [0.46813021474490496, 0.24716181096977158, 0.2... 😅 \n",
"28082 [0.46813021474490496, 0.24716181096977158, 0.2... 😭 \n",
"44462 [0.2691131498470948, 0.4801223241590214, 0.250... 😌 \n",
"60212 [0.6784741144414169, 0.04495912806539509, 0.27... 😂 \n",
"34950 [0.2691131498470948, 0.4801223241590214, 0.250... 😂 \n",
"59462 [0.46813021474490496, 0.24716181096977158, 0.2... 😢 \n",
"19465 [0.46813021474490496, 0.24716181096977158, 0.2... 😅 \n",
"8135 [0.46813021474490496, 0.24716181096977158, 0.2... 😅 \n",
"\n",
" predicted_sentiment \n",
"35671 [0.4447824954986572, 0.30056363344192505, 0.27... \n",
"25683 [0.5660845637321472, 0.1737498641014099, 0.284... \n",
"8985 [0.4871470034122467, 0.26607102155685425, 0.27... \n",
"5410 [0.4061833620071411, 0.3226468861103058, 0.273... \n",
"62611 [0.4549962878227234, 0.21886931359767914, 0.33... \n",
"48197 [0.6561306715011597, 0.11821962147951126, 0.27... \n",
"23654 [0.4600130021572113, 0.28595462441444397, 0.27... \n",
"58207 [0.49543458223342896, 0.25571855902671814, 0.2... \n",
"374 [0.6040589213371277, 0.15823380649089813, 0.26... \n",
"26310 [0.452500581741333, 0.2882971167564392, 0.2790... \n",
"30892 [0.4644194543361664, 0.2708289325237274, 0.277... \n",
"11868 [0.7254493236541748, 0.12355809658765793, 0.22... \n",
"46219 [0.3264158070087433, 0.48023173213005066, 0.23... \n",
"13583 [0.4271591007709503, 0.29361462593078613, 0.29... \n",
"43843 [0.44168680906295776, 0.2790682315826416, 0.29... \n",
"63589 [0.41863512992858887, 0.3106093108654022, 0.28... \n",
"53695 [0.5650997757911682, 0.19236208498477936, 0.27... \n",
"67529 [0.45511549711227417, 0.28582143783569336, 0.2... \n",
"25493 [0.4276219606399536, 0.30413898825645447, 0.28... \n",
"19486 [0.7149834036827087, 0.10459273308515549, 0.24... \n",
"48449 [0.5363025665283203, 0.22163532674312592, 0.27... \n",
"4504 [0.4641677737236023, 0.18824045360088348, 0.37... \n",
"40285 [0.44292521476745605, 0.28201037645339966, 0.2... \n",
"56741 [0.40168094635009766, 0.3777309060096741, 0.24... \n",
"22948 [0.4365620017051697, 0.2830066680908203, 0.294... \n",
"68426 [0.3452186584472656, 0.4593580365180969, 0.223... \n",
"13431 [0.4436468482017517, 0.2736954987049103, 0.294... \n",
"66287 [0.47334975004196167, 0.2881445586681366, 0.26... \n",
"41980 [0.4635038673877716, 0.30251604318618774, 0.26... \n",
"34632 [0.43297499418258667, 0.31000325083732605, 0.2... \n",
"... ... \n",
"47218 [0.470976859331131, 0.2997904419898987, 0.2489... \n",
"35087 [0.4478594660758972, 0.30181050300598145, 0.27... \n",
"61252 [0.5783949494361877, 0.15405726432800293, 0.30... \n",
"39960 [0.4938848316669464, 0.24237176775932312, 0.25... \n",
"365 [0.6203110218048096, 0.17863908410072327, 0.23... \n",
"50665 [0.4619033932685852, 0.2977892756462097, 0.267... \n",
"21007 [0.5029815435409546, 0.2711288630962372, 0.253... \n",
"18819 [0.6974080801010132, 0.11416944861412048, 0.24... \n",
"42421 [0.5765037536621094, 0.18341206014156342, 0.26... \n",
"38705 [0.4030645787715912, 0.36543628573417664, 0.25... \n",
"47033 [0.6219494342803955, 0.1795988380908966, 0.226... \n",
"38742 [0.4049544930458069, 0.35102248191833496, 0.25... \n",
"67610 [0.416861355304718, 0.35468244552612305, 0.245... \n",
"61021 [0.4401417672634125, 0.2810788154602051, 0.292... \n",
"14904 [0.5411785244941711, 0.2148503214120865, 0.246... \n",
"50994 [0.7073097229003906, 0.12480126321315765, 0.23... \n",
"6973 [0.6375303864479065, 0.14495858550071716, 0.24... \n",
"3269 [0.5052645802497864, 0.21280284225940704, 0.30... \n",
"54827 [0.5138391852378845, 0.26520460844039917, 0.25... \n",
"19409 [0.520979106426239, 0.22392335534095764, 0.265... \n",
"38703 [0.32009249925613403, 0.4819949269294739, 0.22... \n",
"25133 [0.5703040361404419, 0.17875489592552185, 0.27... \n",
"15339 [0.4571400284767151, 0.2949279546737671, 0.269... \n",
"28082 [0.36291953921318054, 0.4310339391231537, 0.22... \n",
"44462 [0.6122021675109863, 0.15579015016555786, 0.27... \n",
"60212 [0.470508873462677, 0.2711063623428345, 0.2810... \n",
"34950 [0.4431285262107849, 0.27477312088012695, 0.29... \n",
"59462 [0.40375131368637085, 0.3289208710193634, 0.27... \n",
"19465 [0.4494284391403198, 0.31076472997665405, 0.26... \n",
"8135 [0.42708122730255127, 0.31392431259155273, 0.2... \n",
"\n",
"[2620 rows x 5 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(testlist)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* exactly correct labeled sentences:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.1851145038167939\n"
]
}
],
"source": [
"print (sum([1 if sample[1]['teacher'] == sample[1]['predict'] else 0 for sample in testlist.iterrows()]) / testlist.shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* mean squared error:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.02218282, 0.02594105, 0.00323429])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"teacher_sentiments = np.array([sample[1]['teacher_sentiment'] for sample in testlist.iterrows()])\n",
"predicted_sentiments = np.array([sample[1]['predicted_sentiment'] for sample in testlist.iterrows()])\n",
"\n",
"mean_squared_error = ((teacher_sentiments - predicted_sentiments)**2).mean(axis=0)\n",
"display(mean_squared_error)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* by an overall variance of:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variance teacher: [0.02183094 0.02513847 0.00285735]\n",
"Variance prediction: [0.00850173 0.00793481 0.00095984]\n"
]
}
],
"source": [
"print(\"Variance teacher: \", np.var(teacher_sentiments, axis=0))\n",
"print(\"Variance prediction: \", np.var(predicted_sentiments, axis=0))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"testlist.to_csv('test.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* save classifier:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"clf.save(\"clf2.keras\")\n",
"pickle.dump( vectorizer, open( \"vec2.pickle\", \"wb\" ) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"## testing area\n",
"\n",
"**for just testing, start from here!**\n",
"\n",
"download the dumped classifier and vectorizer from [here](https://the-cake-is-a-lie.net/nextcloud/index.php/s/NjMXamfwQsyrefG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* loading classifier and vectorizer"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import clear_output, Markdown, Math\n",
"import ipywidgets as widgets\n",
"import sys\n",
"sys.path.append(\"..\")\n",
"\n",
"from Tools.Emoji_Distance import sentiment_vector_to_emoji\n",
"from Tools.Emoji_Distance import emoji_to_sentiment_vector\n",
"\n",
"def emoji2sent(emoji_arr):\n",
" return np.array([emoji_to_sentiment_vector(e) for e in emoji_arr])\n",
"\n",
"def sent2emoji(sent_arr, custom_target_emojis=None):\n",
" return [sentiment_vector_to_emoji(s, custom_target_emojis=custom_target_emojis) for s in sent_arr]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import keras\n",
"import pickle\n",
"clf = keras.models.load_model(\"clf.keras\")\n",
"vectorizer = pickle.load( open( \"vec.pickle\", \"rb\" ) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* define lookup emojis here:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"lookup_emojis = [#'😂',\n",
" '😭',\n",
" '😍',\n",
" '😩',\n",
" '😊',\n",
" '😘',\n",
" '🙏',\n",
" '🙌',\n",
" '😉',\n",
" '😁',\n",
" '😅',\n",
" '😎',\n",
" '😢',\n",
" '😒',\n",
" '😏',\n",
" '😌',\n",
" '😔',\n",
" '😋',\n",
" '😀',\n",
" '😤']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* a simple output widget for testing:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "755aa31644db4628a3be1ff3b621fa28",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Text(value='')"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4601eccc07074e71983c9005d1d329b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Button(description='get emoji', icon='check', style=ButtonStyle(), tooltip='Click me'), Output(…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"out = widgets.Output()\n",
"\n",
"t = widgets.Text()\n",
"b = widgets.Button(\n",
" description='get emoji',\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",
" pred = clf.predict(vectorizer.transform([t.value]))\n",
" \n",
" display(Markdown(\"# Predicted Emoji \" + str(sent2emoji(pred, lookup_emojis)[0])))\n",
" display(Markdown(\"# Sentiment Vector: $$ \\pmatrix{\" + str(pred[0,0]) +\n",
" \"\\\\\\\\\" + str(pred[0,1]) + \"\\\\\\\\\" + str(pred[0,2]) + \"}$$\"))\n",
"\n",
"b.on_click(handle_submit)\n",
" \n",
"display(t)\n",
"display(widgets.VBox([b, out])) "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'mlb' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-cb3b65f1446b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0my_trans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myt1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mpred_trans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myt1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'mlb' is not defined"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"y_trans = mlb.inverse_transform(yt1)\n",
"pred_trans = mlb.inverse_transform(yt1)\n",
"\n",
"# evaluate accuracy\n",
"pos = 0\n",
"neg = 0\n",
"all = 0\n",
"for entry in range(len(y_trans)):\n",
" if len(np.intersect1d(y_trans[entry], pred_trans[entry])) > 0:\n",
" pos += 1\n",
" else:\n",
" neg += 1\n",
" all += 1\n",
"print(pos/all)\n",
"print(neg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"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
}
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