lesson 4 files added

4.2 Rule Based Sentiment.ipynb

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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4.2 Rule-based Sentiment Analysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Textblob"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from textblob import TextBlob"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sentence_1 = \"i had a great time at the movie it was really funny\"\n",
+ "sentence_2 = \"i had a great time at the movie but the parking was terrible\"\n",
+ "sentence_3 = \"i had a great time at the movie but the parking wasn't great\"\n",
+ "sentence_4 = \"i went to see a movie\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "i had a great time at the movie it was really funny\n",
+ "0.525\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(sentence_1)\n",
+ "sentiment_score = TextBlob(sentence_1)\n",
+ "print(sentiment_score.sentiment.polarity)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TextBlob returns a score between -1 and 1, where negative scores indicate negative sentiment, and positive scores indicate positive sentiment. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "i had a great time at the movie but the parking was terrible\n",
+ "-0.09999999999999998\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(sentence_2)\n",
+ "sentiment_score_2 = TextBlob(sentence_2)\n",
+ "print(sentiment_score_2.sentiment.polarity)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "i had a great time at the movie but the parking wasn't great\n",
+ "0.8\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(sentence_3)\n",
+ "sentiment_score_3 = TextBlob(sentence_3)\n",
+ "print(sentiment_score_3.sentiment.polarity)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "i went to see a movie\n",
+ "0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(sentence_4)\n",
+ "sentiment_score_4 = TextBlob(sentence_4)\n",
+ "print(sentiment_score_4.sentiment.polarity)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### VADER"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vader_sentiment = SentimentIntensityAnalyzer()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "i had a great time at the movie it was really funny\n",
+ "{'neg': 0.0, 'neu': 0.578, 'pos': 0.422, 'compound': 0.807}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(sentence_1)\n",
+ "print(vader_sentiment.polarity_scores(sentence_1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The compond score gives us the overall score for the text and is scored the same as TextBlob between -1 and 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "i had a great time at the movie but the parking was terrible\n",
+ "{'neg': 0.234, 'neu': 0.621, 'pos': 0.144, 'compound': -0.3818}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(sentence_2)\n",
+ "print(vader_sentiment.polarity_scores(sentence_2)) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "i had a great time at the movie but the parking wasn't great\n",
+ "{'neg': 0.247, 'neu': 0.611, 'pos': 0.142, 'compound': -0.4387}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(sentence_3)\n",
+ "print(vader_sentiment.polarity_scores(sentence_3)) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "i went to see a movie\n",
+ "{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(sentence_4)\n",
+ "print(vader_sentiment.polarity_scores(sentence_4)) "
+ ]
+ }
+ ],
+ "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": "3.8.5"
+ }
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+}