From 7d39e34e60a5d95a3e92f11214d58f314371a133 Mon Sep 17 00:00:00 2001 From: nitishabharathi <30657775+nitishabharathi@users.noreply.github.com> Date: Thu, 13 Feb 2020 14:44:31 +0530 Subject: [PATCH] added base code --- Shape of Stories.ipynb | 388 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 388 insertions(+) create mode 100644 Shape of Stories.ipynb diff --git a/Shape of Stories.ipynb b/Shape of Stories.ipynb new file mode 100644 index 0000000..6d67188 --- /dev/null +++ b/Shape of Stories.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from textblob import TextBlob\n", + "import nltk\n", + "from nltk.corpus import stopwords\n", + "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.animation as animation\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Book content source: https://www.gutenberg.org" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#Reading content of the novel\n", + "\n", + "with open('dracula.txt', 'r') as file:\n", + " lines = file.readlines()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Text Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#Removing new lines\n", + "\n", + "for i in lines: \n", + " if (i == '\\n'):\n", + " lines.remove(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#Removing whitespaces, digits and punctuations\n", + "\n", + "def preprocessing(text):\n", + " punctuations = '''()-[]{};:'\"\\,<>./?@#$%^&*_~'''\n", + " for x in text.lower(): \n", + " if x in punctuations: \n", + " text = text.replace(x,\" \") \n", + " \n", + " text = re.sub(r'\\d+', '', text) \n", + " text = text.rstrip()\n", + " text = \" \".join(text.split()) \n", + " return text" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "preprocessed_lines=[]\n", + "\n", + "for i in lines:\n", + " preprocessed_lines.append(preprocessing(i))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "input_string = \"\".join(preprocessed_lines)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "words = input_string.split()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#Removing stopwords defined in NLTK\n", + "\n", + "stop_words = set(stopwords.words('english')) \n", + "for i in words:\n", + " if(len(i)<2 or i in stop_words):\n", + " words.remove(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "book_content = \" \".join(words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Sliding Window for the Content" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#Funtions to split words and merge them with a sliding window\n", + "\n", + "\n", + "def sample_window(seq, window_size = 100, stride = 10):\n", + " for pos in range(0, len(seq), stride):\n", + " yield seq[pos : pos + window_size]\n", + " \n", + "def merge_words(seq, stride):\n", + " for pos in range(0, len(seq), stride):\n", + " yield seq[pos : pos + stride]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "words = [s for s in book_content.split()] \n", + "split_sentence = [\" \".join(w) for w in merge_words(words, 3)]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "delim = \" \"\n", + "samples = [delim.join(s) for s in sample_window(split_sentence, 5, 1)]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sentimental Analysis using Vader" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Function that returns the sentiment score\n", + "\n", + "def sentiment_analyzer_scores(sentence):\n", + " score = analyser.polarity_scores(sentence)\n", + " return score['compound']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "analyser = SentimentIntensityAnalyzer()\n", + "scores=[]\n", + "for i in samples:\n", + " scores.append(sentiment_analyzer_scores(i))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.DataFrame()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(\n", + " {'sample': samples,\n", + " 'score': scores,\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.0, 0.0, 0.0, 0.5719, 0.5719, 0.5719, 0.128, 0.128, -0.4939, -0.4939]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores[0:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.iloc[1:200].plot(figsize = (16,8))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating a Animated Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig = plt.figure()\n", + "ax1 = fig.add_subplot(1,1,1)\n", + "\n", + "def animate(i):\n", + " xs=[]\n", + " ys=[]\n", + " data = open('score.txt','r').read()\n", + " lines = data.split('\\n')\n", + " xs = []\n", + " ys = []\n", + " \n", + " for line in lines:\n", + " x, y = line.split(',') \n", + " xs.append(float(x))\n", + " ys.append(float(y))\n", + " \n", + " \n", + " '''\n", + " for i in range(len(samples)):\n", + " xs.append(i)\n", + " ys.append(df['score'][i])\n", + " \n", + " '''\n", + " ax1.clear()\n", + " ax1.plot(xs, ys)\n", + "\n", + " plt.xlabel('text%')\n", + " plt.ylabel('Score')\n", + " plt.title('Shape of Stories')\n", + "\n", + "ani = animation.FuncAnimation(fig, animate, interval=1000) \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenges\n", + "\n", + "- Struck up creating animated plot (my plan is to create a plot where x axis keeps updating slowly)\n", + "- Not sure if the sentimental compound values provided by Valder is accurate\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Work to be done (Shape of Stories)\n", + "\n", + "- Smoothen the plot obtained\n", + "- Create an animated plot\n", + "- Try other Sentimental Analysis Tool/libraries\n", + "- Create UI" + ] + } + ], + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}