{ "metadata": { "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 }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from textblob import TextBlob\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# You should download the book text, clean it, and import it here\n", "with open(\"pride.txt\", encoding=\"utf8\") as f:\n", " file_contents = f.read()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "book_pride = TextBlob(file_contents)\n", "positive_sentiment_sentences = []\n", "negative_sentiment_sentences = []" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for sentence in book_pride.sentences:\n", " if sentence.sentiment.polarity == 1:\n", " positive_sentiment_sentences.append(sentence)\n", " if sentence.sentiment.polarity == -1:\n", " negative_sentiment_sentences.append(sentence)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n", "for sentence in positive_sentiment_sentences:\n", " print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\" \", \" \")))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n", "for sentence in negative_sentiment_sentences:\n", " print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\" \", \" \")))" ] } ] }