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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Basic Sentiment Analysis Using TextBlob"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from textblob import TextBlob"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"feedback1 = \"The Movies Was Awesome\"\n",
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"feedback2 = \"The Movies was Average\"\n",
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"feedback3 = \"The Movie was Good\"\n",
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"feedback4 = \"The Movies Was Intresting\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"blob1 = TextBlob(feedback1)\n",
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"blob2 = TextBlob(feedback2)\n",
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"blob3 = TextBlob(feedback3)\n",
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"blob4 = TextBlob(feedback4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sentiment(polarity=1.0, subjectivity=1.0)\n",
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"Sentiment(polarity=-0.15, subjectivity=0.39999999999999997)\n",
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"Sentiment(polarity=0.7, subjectivity=0.6000000000000001)\n",
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"Sentiment(polarity=0.0, subjectivity=0.0)\n"
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]
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}
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],
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"source": [
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"print(blob1.sentiment)\n",
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"print(blob2.sentiment)\n",
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"print(blob3.sentiment)\n",
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"print(blob4.sentiment)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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