nlp-lab/Project/simple_approach/Evaluation_sentiment_dataset.ipynb

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2018-07-23 09:23:17 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"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"
]
}
],
"source": [
"import numpy as np \n",
"import pandas as pd \n",
"import simple_twitter_learning as stl\n",
"import re"
]
},
{
"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"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('dataset_sentiment.csv')\n",
"df = df[['text','sentiment']]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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"
]
},
"execution_count": 5,
"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",
"execution_count": 6,
"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",
"execution_count": 8,
"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",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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')"
]
},
{
"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
}