examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import textvisualizer as tv\n",
    "import pandas as pd\n",
    "import re\n",
    "import plotly.graph_objects as go\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from nltk.corpus import stopwords\n",
    "import string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Loading 20 News groups corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsgroups = fetch_20newsgroups(subset='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_url(text):\n",
    "    if type(text)!=str:\n",
    "        return(text)\n",
    "    return(re.sub(r'http\\S+', '', text))\n",
    "\n",
    "def strip_html_tags(text):\n",
    "    p = re.compile(r'<.*?>')\n",
    "    return p.sub('', text)\n",
    "\n",
    "def remove_numbers(text):\n",
    "    if type(text)!=str:\n",
    "        return(text)\n",
    "    text = re.sub('[0-9]+','',text)\n",
    "    return(text)\n",
    "\n",
    "def remove_punctuation(text):\n",
    "    return (text.translate(str.maketrans('', '', string.punctuation)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "df['text'] = newsgroups.data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text_clean'] = df['text'].apply(remove_url)\n",
    "df['text_clean'] = df['text_clean'].apply(strip_html_tags)\n",
    "df['text_clean'] = df['text_clean'].apply(remove_numbers)\n",
    "df['text_clean'] = df['text_clean'].apply(remove_punctuation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mystopwords = stopwords.words('english')+['ax','edu','com','would','nntp','ac','co','gv','bf','db','tin','apr','gmt','na','pl','di','inc','gov','max','acs','cs',\n",
    "                                         'subject','lines','organization','writes','article','one','posting','host','ca','also','too','maxaxaxaxaxaxaxaxaxaxaxaxaxaxax']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['labels'] = [newsgroups.target_names[newsgroups.target[i]] for i in newsgroups.target]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting using functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.frequencyPlot(listText=df.text_clean.to_list(), stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.frequencyPlot(listText=df.text_clean.to_list(), ngramRange=(2,2), stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.frequencyPlot(listText=df.text_clean.to_list(), ngramRange=(3,3), stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = tv.frequencyPlotly(listText=df.text_clean.to_list(), stopwords=mystopwords)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = tv.frequencyPlotly(listText=df.text_clean.to_list(), ngramRange=(2,2), stopwords=mystopwords)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = tv.frequencyPlotly(listText=df.text_clean.to_list(), ngramRange=(3,3), stopwords=mystopwords)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.frequencyPlotYellowbrick(listText=df.text_clean.to_list(), stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.frequencyPlotYellowbrick(listText=df.text_clean.to_list(), ngramRange=(2,2), stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.frequencyPlotYellowbrick(listText=df.text_clean.to_list(), ngramRange=(3,3), stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tree map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = tv.frequencyTreeMap(listText=df.text_clean.to_list(), stopwords=mystopwords)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = tv.frequencyTreeMap(listText=df.text_clean.to_list(), ngramRange=(2,2), stopwords=mystopwords)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = tv.frequencyTreeMap(listText=df.text_clean.to_list(), ngramRange=(3,3), stopwords=mystopwords)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donut chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = tv.frequencyDonutChart(listText=df.text_clean.to_list(), stopwords=mystopwords)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = tv.frequencyDonutChart(listText=df.text_clean.to_list(), ngramRange=(2,2), stopwords=mystopwords)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = tv.frequencyDonutChart(listText=df.text_clean.to_list(), ngramRange=(3,3), stopwords=mystopwords)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phrase net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.phraseNet(connectors=[\"and\"], listText=df.text.to_list(), number_of_pairs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.phraseNet(connectors=[\"at\", \"for\",\"to\",\"from\"], listText=df.text.to_list(), number_of_pairs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.phraseNet(connectors=[\"for the\"], listText=df.text.to_list(), number_of_pairs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = tv.phraseNetPlotly(connectors=[\"and\"], listText=df.text.to_list())\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = tv.phraseNetPlotly(connectors=[\"at\", \"for\",\"to\",\"from\"], listText=df.text.to_list())\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = tv.phraseNetPlotly(connectors=[\"for the\"], listText=df.text.to_list())\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.wordcloudPlot( ' '.join(df.text.to_list()),mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.vennWordcloudPlot(df.text_clean.to_list(), df.labels.to_list(), [\"rec.autos\",\"rec.motorcycles\"],mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.vennWordcloudPlot(df.text_clean.to_list(), df.labels.to_list(), [\"rec.autos\",\"rec.motorcycles\",\"sci.space\"],mystopwords) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.wordTree(df.text.to_list(),\"people\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.wordTree(df.text.to_list(),\"universe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Bubble Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.bubbleChart(df.text_clean.tolist(), number_of_words=25, stopwords=mystopwords, palette = 'red' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.bubbleChart(df[df.labels=='sci.space'].text_clean.tolist(), number_of_words=25, stopwords=mystopwords, palette = 'green', title = 'sci.space' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.bubbleChart(df[df.labels=='sci.electronics'].text_clean.tolist(), number_of_words=25, stopwords=mystopwords, palette = 'blue', title = 'sci.electronics' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lexical Dispersion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.lexicalDispersionPlot(df.text_clean.tolist(), ['universe','laptop','economy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.lexicalDispersionPlot(df[df.labels == 'sci.space'].text_clean.tolist(), ['universe','earth','sun'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.wordCorrelationsPlot(df.text_clean.tolist(), ['computer','laptop','universe','physical','economy','inflation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv.wordCorrelationsPlot(df[df.labels == 'sci.space'].text_clean.tolist(), ['universe','earth','sun','moon'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bigram Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tv.bigramGraph(df.text_clean.tolist(), mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tv.bigramGraph(df[df.labels == 'sci.space'].text_clean.tolist(), mystopwords, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Plotting using Class Corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Create corpus object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = tv.Corpus(df.text_clean.tolist(), df.labels.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyPlot(stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyPlot(stopwords=mystopwords, labels = \"sci.space\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyPlot(stopwords=mystopwords, labels = [\"rec.autos\",\"rec.motorcycles\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyPlot(stopwords=mystopwords, package = 'plotly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyPlot(stopwords=mystopwords, labels = \"sci.space\", package = 'plotly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyPlot(stopwords=mystopwords, labels = [\"rec.autos\",\"rec.motorcycles\"], package = 'plotly')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyPlot(stopwords=mystopwords, package = 'yellowbrick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyPlot(stopwords=mystopwords, labels = \"sci.space\", package = 'yellowbrick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyPlot(stopwords=mystopwords, labels = [\"rec.autos\",\"rec.motorcycles\"], package = 'yellowbrick')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tree Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyTreeMap(stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyTreeMap(stopwords=mystopwords, labels = \"sci.space\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyTreeMap(stopwords=mystopwords, labels = [\"rec.autos\",\"rec.motorcycles\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.frequencyTreeMap(stopwords=mystopwords, ngramRange=(2,2), labels = [\"rec.autos\",\"rec.motorcycles\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donut chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "c.frequencyDonutChart(stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "c.frequencyDonutChart(ngramRange=(2,2), stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "c.frequencyDonutChart(ngramRange=(3,3), stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phrase Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.phraseNet(connectors=[\"at\"],number_of_pairs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.phraseNet(connectors=[\"at\"],number_of_pairs=15, labels = \"sci.space\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.phraseNet(connectors=[\"at\"],number_of_pairs=15, labels = [\"rec.autos\",\"rec.motorcycles\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.phraseNet(connectors=[\"at\"],number_of_pairs=15, plotly=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.phraseNet(connectors=[\"at\"],number_of_pairs=15, labels = \"sci.space\", plotly=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.phraseNet(connectors=[\"at\"],number_of_pairs=15, labels = [\"rec.autos\",\"rec.motorcycles\"], plotly=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.wordcloudPlot(stopwords=mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.wordcloudPlot(stopwords=mystopwords, labels = \"sci.space\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.wordcloudPlot(stopwords=mystopwords, labels = [\"rec.autos\",\"rec.motorcycles\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.vennWordcloudPlot([\"sci.electronics\",\"sci.space\"],mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.vennWordcloudPlot([\"comp.sys.mac.hardware\",\"comp.sys.ibm.pc.hardware\",\"comp.os.ms-windows.misc\"],mystopwords) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.wordTree(\"laptop\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.wordTree(\"economy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bubble Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.bubbleChart(number_of_words=25, stopwords=mystopwords, palette = 'red' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.bubbleChart(number_of_words=25, stopwords=mystopwords, palette = 'green', title = 'sci.space', labels = 'sci.space')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.bubbleChart(number_of_words=25, stopwords=mystopwords, palette = 'blue', title = 'sci.electronics', labels = 'sci.electronics' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lexical Dispersion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.lexicalDispersionPlot(['universe','laptop','economy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.lexicalDispersionPlot(['universe','earth','sun'], labels = 'sci.space')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.labels.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.wordCorrelationsPlot(['computer','laptop','universe','physical','economy','inflation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "c.wordCorrelationsPlot(['universe','earth','sun','moon'], labels='sci.space')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bigram Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "c.bigramGraph(mystopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "c.bigramGraph(mystopwords, ['sci.space'], 20)"
   ]
  }
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