nlp-lab/Project/simple_approach/Evaluation_sentiment_dataset.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
"import numpy as np \n",
"import pandas as pd \n",
"import simple_twitter_learning as stl\n",
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"import re\n",
"import matplotlib.pyplot as plt"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* download data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dataset already downloaded\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"if [ ! -e 'dataset_sentiment.csv' ]\n",
"then\n",
" echo \"downloading dataset\"\n",
" wget https://raw.githubusercontent.com/SmartDataAnalytics/MA-INF-4222-NLP-Lab/master/2018_SoSe/exercises/dataset_sentiment.csv\n",
"else\n",
" echo \"dataset already downloaded\"\n",
"fi"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"* plot function:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"def sentiment_score(s):\n",
" #(pos, neg, neu)^T\n",
" return s[0] - s[1]\n",
"\n",
"def plot_sentiment_space(predicted_sentiment_vectors_list, top_sentiments, top_emojis, style=['bo']):\n",
" # sentiment score axis\n",
" top_X = np.array([sentiment_score(x) for x in top_sentiments])\n",
" \n",
"\n",
" # neutral axis:\n",
" top_Y = np.array([x[2] for x in top_sentiments])\n",
" \n",
"\n",
" fig_1, ax_1 = plt.subplots(figsize=(15,10))\n",
" plt.title(\"sentiment-score-plot\")\n",
" plt.xlabel(\"sentiment score\")\n",
" plt.ylabel(\"neutrality\")\n",
" plt.xlim([-1,1])\n",
" plt.ylim([0,1])\n",
" for i in range(len(top_X)):\n",
" plt.text(top_X[i], top_Y[i], top_emojis[i])\n",
" for i in range(len(predicted_sentiment_vectors_list)):\n",
" pred_X = np.array([sentiment_score(x) for x in predicted_sentiment_vectors_list[i]])\n",
" pred_Y = np.array([x[2] for x in predicted_sentiment_vectors_list[i]])\n",
" plt.plot(pred_X, pred_Y, style[i], alpha=0.5)\n",
" #plt.savefig(\"val-error_sentiment-plot\" + str(datetime.datetime.now()) + \".png\", bbox_inches='tight')\n",
"\n",
" # sentiment score axis\n",
" top_X = np.array([x[0] for x in top_sentiments])\n",
" \n",
"\n",
" # neutral axis:\n",
" top_Y = np.array([x[1] for x in top_sentiments])\n",
" \n",
"\n",
" fig_2, ax_2 = plt.subplots(figsize=(15,10))\n",
" plt.title(\"positive-negative-plot\")\n",
" plt.xlabel(\"positive\")\n",
" plt.ylabel(\"negative\")\n",
" plt.xlim([0,1])\n",
" plt.ylim([0,1])\n",
" for i in range(len(top_X)):\n",
" plt.text(top_X[i], top_Y[i], top_emojis[i])\n",
" for i in range(len(predicted_sentiment_vectors_list)):\n",
" pred_X = np.array([x[0] for x in predicted_sentiment_vectors_list[i]])\n",
" pred_Y = np.array([x[1] for x in predicted_sentiment_vectors_list[i]])\n",
" plt.plot(pred_X, pred_Y, style[i], alpha=0.5)\n",
" #'plt.savefig(\"val-error_positive-negative-plot\" + str(datetime.datetime.now()) + \".png\", bbox_inches='tight')\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* read data:"
]
},
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{
"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('dataset_sentiment.csv')\n",
"df = df[['text','sentiment']]"
]
},
{
"cell_type": "code",
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"execution_count": 39,
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"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>sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>nancyleegrahn how did everyone feel about th...</td>\n",
" <td>Neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>scottwalker didnt catch the full gopdebate l...</td>\n",
" <td>Positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>tjmshow no mention of tamir rice and the gop...</td>\n",
" <td>Neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>robgeorge that carly fiorina is trending ho...</td>\n",
" <td>Positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>danscavino gopdebate w realdonaldtrump deliv...</td>\n",
" <td>Positive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text sentiment\n",
"0 nancyleegrahn how did everyone feel about th... Neutral\n",
"1 scottwalker didnt catch the full gopdebate l... Positive\n",
"2 tjmshow no mention of tamir rice and the gop... Neutral\n",
"3 robgeorge that carly fiorina is trending ho... Positive\n",
"4 danscavino gopdebate w realdonaldtrump deliv... Positive"
]
},
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"execution_count": 39,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['text'] = df['text'].apply(lambda x: x.lower())\n",
"df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))\n",
"df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-Z0-9\\s]','',x)))\n",
"df.head()"
]
},
{
"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
"outputs": [],
"source": [
"neg = np.array([df['sentiment'][i] == 'Negative' for i in range(df.shape[0])])\n",
"pos = np.array([df['sentiment'][i] == 'Positive' for i in range(df.shape[0])])\n",
"neu = np.array([df['sentiment'][i] == 'Neutral' for i in range(df.shape[0])])\n",
"\n",
"text = np.array(df['text'].tolist())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* load pipeline"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"pipeline_file = \"/home/jonas/Dokumente/NLP_DATA/python_dumps/pipelines/tfidf_final/final_epoch01.pipeline\"\n",
"pm = stl.pipeline_manager.load_from_pipeline_file(pipeline_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* plot statements"
]
},
{
"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
"outputs": [],
"source": [
"top_20 = list(\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\")\n",
"top_20_sents = stl.emoji2sent(top_20)\n",
"\n",
"pred_pos = pm.predict(text[pos])\n",
"pred_neg = pm.predict(text[neg])\n",
"pred_neu = pm.predict(text[neu])"
]
},
{
"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1080x720 with 1 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1080x720 with 1 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
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}
],
"source": [
"#stl.plot_sentiment_space(predicted_sentiment_vectors=pred_pos, top_sentiments=top_20_sents, top_emojis=top_20, style='go')\n",
"#stl.plot_sentiment_space(predicted_sentiment_vectors=pred_neg, top_sentiments=top_20_sents, top_emojis=top_20, style='ro')\n",
"#stl.plot_sentiment_space(predicted_sentiment_vectors=pred_neu, top_sentiments=top_20_sents, top_emojis=top_20, style='bo')\n",
"plot_sentiment_space(predicted_sentiment_vectors_list=[pred_pos,pred_neg,pred_neu], top_sentiments=top_20_sents, top_emojis=top_20, style=['g+','b+','r+'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## user labeled sentiment dataset"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../sentiment_sentences.csv', delimiter=';')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"neg = np.array([df['sent'][i] == 'n' for i in range(df.shape[0])])\n",
"pos = np.array([df['sent'][i] == 'p' for i in range(df.shape[0])])\n",
"\n",
"text = np.array(df['text'].tolist())\n",
"\n",
"pred_pos = pm.predict(text[pos])\n",
"pred_neg = pm.predict(text[neg])"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
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{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1080x720 with 1 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAA4AAAAJcCAYAAACov8q3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XuYXXV9L/73N0GaqJF4AQkGmlixiNoHPKPiT+Q3qNQoiBYJJQY1sSqlUo2XI1oNjFFLm2ABj6KHFsF6AfHE1oDg5VQGLxV0kKhACpIh1igpBIVAyt3v+WP2hElIwkyYPXsy6/V6nv3M3mutvdZnkz983n4vn1JrDQAAABPfpE4XAAAAwNgQAAEAABpCAAQAAGgIARAAAKAhBEAAAICGEAABAAAaQgAEoKNKKZ8ppSzezvm/KaX801jWNNZKKfNLKd/qwHN7SilfGOvnAtA5RR9AAMaLUkp3ki/UWmd2upZ2KaXMSnJTksfUWh/ocC09SZ5Raz1uGNeuSfKWWuv/bXddALSPEUAAAICGEAABGJFSyppSygdKKdeVUn5XSjm3lDKlde6tpZQbSym/LaWsKKXs1TpeSimnl1JuKaVsKKX8vJTynNa580opHy2lPC7JpUn2KqXc1XrtNXSaYinl0lLKiVvU89NSylGt9/uVUr7dev71pZRjtvM7ukspa0sp72nVdXMpZeGQ839QSjmtlPKfpZT/ak1VnTrk/Pta3/lNKeUtpZRaSnlG69zhpZSrW7/1V62RtkHfbf29vfUbX1RKWVBK+X7ru58upZy2Ra1fK6W8u/V+r1LK8lLKraWUm0op79jOb5zVquttrTpvLqW8dzvXH1lKubaUcnsppbeU8qzW8c8n2SfJRa2a37etewAwvgmAAOyI+UlekeSPkjwzyYdKKS9NcmqSY5LMSPLLJBe0rv/TJIe0rt2tdc1tQ29Ya92Y5JVJflNrfXzr9Zstnnt+knmDH0op+yf5wyRfbwXIbyf5UpI9khyb5KzWNduyZ6uepyX5iySfKqU8sXXu71r1HpDkGa1rTm49d06Sdyd5eetc9xb33ZjkjUmmJzk8yQmllNe2zh3S+ju99Rt/uJXf+OellNJ61hMz8N/vglLKpCQXJflpq56XJVlUSnnFdn5jkhyaZN/WfU4qpbx8ywtKKc9sPXtRkt2TXJKBwLdrrfUNSf4zyatbNS99hOcBME4JgADsiE/WWn9Va/1tko9lIJTNT/LZWutPaq33JvlAkhe11rzdn2Rakv0ysP58Va315h147r8kOaCU8oetz/OTfLX1vCOSrKm1nltrfaDWenWS5Unmbud+9ydZUmu9v9Z6SZK7kvxxK3y9Lcm7aq2/rbXemeRvMxAqk4EAe26t9dpa638n6Rl601prb63157XW39daf5aBYPX/D/M3fi9JTfKS1uejk/ywFYafn2T3WuuSWut9tdb+JP84pK5t+XCtdWOt9edJzs2QED3Enyf5eq3127XW+5OclmRqkv9vmHUDsBMQAAHYEb8a8v6XSfZqvX45eLDWelcGRvmeVmv9TpJPJvlUkltKKWeXUp4w0oe2gtjX81DgmZfki633f5jkha3pi7eXUm7PQEDcs5Syz5BppXcNueVtW2zE8t9JHp+BEbDHJrlqyL2+0Tqe1m8d+t9g6PuUUl5YSrmsNU3zjiR/meQpw/yNNQMjp4Mh7fVb/Ma9tviNf5Pkqa3n3jXktc826hv899rSlv9+v29972nDqRuAnYMACMCO2HvI+32S/Kb1GhyZS2tK5pOT/DpJaq2fqLX+jyT7Z2Bq5f/cyn2HszX1+UnmlVJelGRKkstax3+V5PJa6/Qhr8fXWk+otf7nkGmljx/GM9YnuTvJs4fca7ch3705ydCdSvfe4vtfSrIiyd611t2SfCZJGeFvPLo10vnCDIxkDv7Gm7b4jdNqra9KkqG/sdb6n9uob/Dfa0tb/vuV1vd+PYK6ARjnBEAAdsTbSykzSylPSvLBJF/OQGhZWEo5oJTyBxmYMnllrXVNKeX5rVGxx2Rgfdw9SX6/lfv+V5Inl1J2286zL8lAUFmS5MutkaokuTjJM0spbyilPKb1ev7gRiYj0brnPyY5vZSyR5KUUp42ZK3dha3f+qxSymOTbNnHcFqS39Za7ymlvCADo3iDbs3Ab3/6dp5/dQZC6D8l+Wat9fbWqR8lubOUclIpZWopZXIp5TmllOc/wk9aXEp5bCnl2UkWZuDfa0sXJjm8lPKy1r/Te5Lcm+TfW+f/a3s1A7BzEAAB2BFfSvKtJP1JVif5aKs/3OIMjFbdnIENYganaj4hA4HqdxmYZnhbkmVb3rTW+h8ZCJL9rSmOD5uq2Frv99UMbMDypSHH78zAJifHZmA0a12Sv0/yBzv4G09KcmOSK0opG5L83yR/3HrWpUk+kYHRxxuTXNH6zr2tv3+VZEkp5c4MbBxz4ZA6/zsD6yZ/0PqNB23j+V/aym98MANrHQ/IQC/BwZC4vcCcJJe36vy3JKfVWh/WdL7Wen2S45L8r9Z9X52BTV/ua11yagY2+7l9ezuJAjC+aQQPwIgUDcEfpjXKeE2SP+h0c/ehyjhqOg/A+GAEEAB2QCnlz8pAr8AnZmCk8SIhC4Dxrm0BsJTy2TLQWPeabZwvpZRPlIGGwT8rpTyvXbUAQBscn+SWDEyBfTDJCZ0tBwAeWdumgJZSDslAP6V/rrU+ZyvnX5Xkr5O8KgM7nJ1Za31hW4oBAACgfSOAtdbvJvntdi55TQbCYa21XpFkeillRrvqAQAAaLpdOvjsp2XzxrRrW8du3vLCUsrbkrwtSR73uMf9j/32229MCgQAABhvrrrqqvW11t135LudDIDDVms9O8nZSdLV1VX7+vo6XBEAAEBnlFJ+uaPf7eQuoL9OsveQzzNbxwAAAGiDTgbAFUne2NoN9KAkd9RaHzb9EwAAgNHRtimgpZTzk3QneUopZW2SU5I8JklqrZ9JckkGdgC9Mcl/J1nYrloAAABoYwCstc57hPM1ydvb9XwAAAA218kpoAAAAIwhARAAAKAhBEAAAICGEAABAAAaQgAEAABoCAEQAACgIQRAAACAhhAAAQAAGkIABAAAaAgBEAAAoCEEQAAAgIYQAAEAABpCAAQAAGgIARAAAKAhBEAAAICGEAABAAAaQgAEAABoCAEQAACgIQRAAACAhhAAAQAAGkIABAAAaAgBEAAAoCEEQAAAgIYQAAEAABpCAAQAAGgIARAAAKAhBEAAAICGEAABAAAaQgAEAABoCAEQAACgIQRAAACAhhAAAQAAGkIABAAAaAgBEAAAoCEEQAAAgIYQAAEAABpCAAQAAGgIARAAAKAhBEAAAICGEAABAAAaQgAEAABoCAEQAACgIQRAAACAhhAAAQAAGkIABAAAaAgBEAAAoCEEQAAAgIYQAAEAABpCAAQAAGgIARAAAKAhBEAAAICGEAABAAAaQgAEAABoCAEQAACgIQRAAACAhhAAAQAAGkIABAAAaAgBEAAAoCEEQAAAgIYQAAEAABpCAAQAAGgIARAAAKAhBEAAAICGEAABAAAaQgAEAABoCAEQAACgIQRAAACAhhAAAQAAGkIABAAAaAgBEAAAoCEEQAAAgIYQAAEAABpCAAQAAGgIARAAAKAhBEAAAICGEAABAAAaQgAEAABoCAEQAACgIQRAAACAhhAAAQAAGkIABAAAaAgBEAAAoCEEQAAAgIYQAAEAABpCAAQAAGgIARAAAKAhBEAAAICGEAABAAAaQgAEAABoCAEQAACgIQRAAACAhhAAAQAAGkIABAAAaAgBEAAAoCEEQAAAgIYQAAEAABpCAAQAAGgIARAAAKAhBEAAAICGEAABAAAaoq0BsJQyp5RyfSnlxlLK+7dyfp9SymWllKtLKT8rpbyqnfUAAAA0WdsCYCllcpJPJXllkv2TzCul7L/FZR9KcmGt9cAkxyY5q131AAAANF07RwBfkOTGWmt/rfW+JBckec0W19QkT2i93y3Jb9pYDwAAQKO1MwA+Lcmvhnxe2zo2VE+S40opa5N
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"text/plain": [
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"<Figure size 1080x720 with 1 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"plot_sentiment_space(predicted_sentiment_vectors_list=[pred_pos,pred_neg], top_sentiments=top_20_sents, top_emojis=top_20, style=['g+','r+'])"
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]
},
{
"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
}