{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, all of the analysis we've done has been pretty generic - looking at counts, creating scatter plots, etc. These techniques could be applied to numeric data as well.\n",
    "\n",
    "When it comes to text data, there are a few popular techniques that we'll be going through in the next few notebooks, starting with sentiment analysis. A few key points to remember with sentiment analysis.\n",
    "\n",
    "1. **TextBlob Module:** Linguistic researchers have labeled the sentiment of words based on their domain expertise. Sentiment of words can vary based on where it is in a sentence. The TextBlob module allows us to take advantage of these labels.\n",
    "2. **Sentiment Labels:** Each word in a corpus is labeled in terms of polarity and subjectivity (there are more labels as well, but we're going to ignore them for now). A corpus' sentiment is the average of these.\n",
    "   * **Polarity**: How positive or negative a word is. -1 is very negative. +1 is very positive.\n",
    "   * **Subjectivity**: How subjective, or opinionated a word is. 0 is fact. +1 is very much an opinion.\n",
    "\n",
    "For more info on how TextBlob coded up its [sentiment function](https://planspace.org/20150607-textblob_sentiment/).\n",
    "\n",
    "Let's take a look at the sentiment of the various transcripts, both overall and throughout the comedy routine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment of Routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transcript</th>\n",
       "      <th>full_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ali</th>\n",
       "      <td>Ladies and gentlemen, please welcome to the st...</td>\n",
       "      <td>Ali Wong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anthony</th>\n",
       "      <td>Thank you. Thank you. Thank you, San Francisco...</td>\n",
       "      <td>Anthony Jeselnik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill</th>\n",
       "      <td>[cheers and applause] All right, thank you! Th...</td>\n",
       "      <td>Bill Burr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bo</th>\n",
       "      <td>Bo What? Old MacDonald had a farm E I E I O An...</td>\n",
       "      <td>Bo Burnham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dave</th>\n",
       "      <td>This is Dave. He tells dirty jokes for a livin...</td>\n",
       "      <td>Dave Chappelle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hasan</th>\n",
       "      <td>[theme music: orchestral hip-hop] [crowd roars...</td>\n",
       "      <td>Hasan Minhaj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jim</th>\n",
       "      <td>[Car horn honks] [Audience cheering] [Announce...</td>\n",
       "      <td>Jim Jefferies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joe</th>\n",
       "      <td>[rock music playing] [audience cheering] [anno...</td>\n",
       "      <td>Joe Rogan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>john</th>\n",
       "      <td>All right, Petunia. Wish me luck out there. Yo...</td>\n",
       "      <td>John Mulaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>louis</th>\n",
       "      <td>Intro\\nFade the music out. Let’s roll. Hold th...</td>\n",
       "      <td>Louis C.K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mike</th>\n",
       "      <td>Wow. Hey, thank you. Thanks. Thank you, guys. ...</td>\n",
       "      <td>Mike Birbiglia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ricky</th>\n",
       "      <td>Hello. Hello! How you doing? Great. Thank you....</td>\n",
       "      <td>Ricky Gervais</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                transcript         full_name\n",
       "ali      Ladies and gentlemen, please welcome to the st...          Ali Wong\n",
       "anthony  Thank you. Thank you. Thank you, San Francisco...  Anthony Jeselnik\n",
       "bill     [cheers and applause] All right, thank you! Th...         Bill Burr\n",
       "bo       Bo What? Old MacDonald had a farm E I E I O An...        Bo Burnham\n",
       "dave     This is Dave. He tells dirty jokes for a livin...    Dave Chappelle\n",
       "hasan    [theme music: orchestral hip-hop] [crowd roars...      Hasan Minhaj\n",
       "jim      [Car horn honks] [Audience cheering] [Announce...     Jim Jefferies\n",
       "joe      [rock music playing] [audience cheering] [anno...         Joe Rogan\n",
       "john     All right, Petunia. Wish me luck out there. Yo...      John Mulaney\n",
       "louis    Intro\\nFade the music out. Let’s roll. Hold th...        Louis C.K.\n",
       "mike     Wow. Hey, thank you. Thanks. Thank you, guys. ...    Mike Birbiglia\n",
       "ricky    Hello. Hello! How you doing? Great. Thank you....     Ricky Gervais"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We'll start by reading in the corpus, which preserves word order\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_pickle('corpus.pkl')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transcript</th>\n",
       "      <th>full_name</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ali</th>\n",
       "      <td>Ladies and gentlemen, please welcome to the st...</td>\n",
       "      <td>Ali Wong</td>\n",
       "      <td>0.069359</td>\n",
       "      <td>0.482403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anthony</th>\n",
       "      <td>Thank you. Thank you. Thank you, San Francisco...</td>\n",
       "      <td>Anthony Jeselnik</td>\n",
       "      <td>0.055237</td>\n",
       "      <td>0.558976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill</th>\n",
       "      <td>[cheers and applause] All right, thank you! Th...</td>\n",
       "      <td>Bill Burr</td>\n",
       "      <td>0.016479</td>\n",
       "      <td>0.537016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bo</th>\n",
       "      <td>Bo What? Old MacDonald had a farm E I E I O An...</td>\n",
       "      <td>Bo Burnham</td>\n",
       "      <td>0.074514</td>\n",
       "      <td>0.539368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dave</th>\n",
       "      <td>This is Dave. He tells dirty jokes for a livin...</td>\n",
       "      <td>Dave Chappelle</td>\n",
       "      <td>-0.002690</td>\n",
       "      <td>0.513958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hasan</th>\n",
       "      <td>[theme music: orchestral hip-hop] [crowd roars...</td>\n",
       "      <td>Hasan Minhaj</td>\n",
       "      <td>0.086856</td>\n",
       "      <td>0.460619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jim</th>\n",
       "      <td>[Car horn honks] [Audience cheering] [Announce...</td>\n",
       "      <td>Jim Jefferies</td>\n",
       "      <td>0.044224</td>\n",
       "      <td>0.523382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joe</th>\n",
       "      <td>[rock music playing] [audience cheering] [anno...</td>\n",
       "      <td>Joe Rogan</td>\n",
       "      <td>0.004968</td>\n",
       "      <td>0.551628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>john</th>\n",
       "      <td>All right, Petunia. Wish me luck out there. Yo...</td>\n",
       "      <td>John Mulaney</td>\n",
       "      <td>0.082355</td>\n",
       "      <td>0.484137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>louis</th>\n",
       "      <td>Intro\\nFade the music out. Let’s roll. Hold th...</td>\n",
       "      <td>Louis C.K.</td>\n",
       "      <td>0.056665</td>\n",
       "      <td>0.515796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mike</th>\n",
       "      <td>Wow. Hey, thank you. Thanks. Thank you, guys. ...</td>\n",
       "      <td>Mike Birbiglia</td>\n",
       "      <td>0.092927</td>\n",
       "      <td>0.518476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ricky</th>\n",
       "      <td>Hello. Hello! How you doing? Great. Thank you....</td>\n",
       "      <td>Ricky Gervais</td>\n",
       "      <td>0.066489</td>\n",
       "      <td>0.497313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                transcript         full_name  \\\n",
       "ali      Ladies and gentlemen, please welcome to the st...          Ali Wong   \n",
       "anthony  Thank you. Thank you. Thank you, San Francisco...  Anthony Jeselnik   \n",
       "bill     [cheers and applause] All right, thank you! Th...         Bill Burr   \n",
       "bo       Bo What? Old MacDonald had a farm E I E I O An...        Bo Burnham   \n",
       "dave     This is Dave. He tells dirty jokes for a livin...    Dave Chappelle   \n",
       "hasan    [theme music: orchestral hip-hop] [crowd roars...      Hasan Minhaj   \n",
       "jim      [Car horn honks] [Audience cheering] [Announce...     Jim Jefferies   \n",
       "joe      [rock music playing] [audience cheering] [anno...         Joe Rogan   \n",
       "john     All right, Petunia. Wish me luck out there. Yo...      John Mulaney   \n",
       "louis    Intro\\nFade the music out. Let’s roll. Hold th...        Louis C.K.   \n",
       "mike     Wow. Hey, thank you. Thanks. Thank you, guys. ...    Mike Birbiglia   \n",
       "ricky    Hello. Hello! How you doing? Great. Thank you....     Ricky Gervais   \n",
       "\n",
       "         polarity  subjectivity  \n",
       "ali      0.069359      0.482403  \n",
       "anthony  0.055237      0.558976  \n",
       "bill     0.016479      0.537016  \n",
       "bo       0.074514      0.539368  \n",
       "dave    -0.002690      0.513958  \n",
       "hasan    0.086856      0.460619  \n",
       "jim      0.044224      0.523382  \n",
       "joe      0.004968      0.551628  \n",
       "john     0.082355      0.484137  \n",
       "louis    0.056665      0.515796  \n",
       "mike     0.092927      0.518476  \n",
       "ricky    0.066489      0.497313  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create quick lambda functions to find the polarity and subjectivity of each routine\n",
    "# Terminal / Anaconda Navigator: conda install -c conda-forge textblob\n",
    "from textblob import TextBlob\n",
    "\n",
    "pol = lambda x: TextBlob(x).sentiment.polarity\n",
    "sub = lambda x: TextBlob(x).sentiment.subjectivity\n",
    "\n",
    "# Apply a function along an axis of the DataFrame.\n",
    "data['polarity'] = data['transcript'].apply(pol)\n",
    "data['subjectivity'] = data['transcript'].apply(sub)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a simple scatter plot of Polarity and Subjectivity\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 8] # Set width to 10 inches and height to 8 inches\n",
    "\n",
    "for index, comedian in enumerate(data.index):\n",
    "    x = data.polarity.loc[comedian]\n",
    "    y = data.subjectivity.loc[comedian]\n",
    "    plt.scatter(x, y, color='blue')\n",
    "    plt.text(x+.001, y+.001, data['full_name'][index], fontsize=10) # Offset the label to avoid overlap of label & dot\n",
    "    plt.xlim(-.01, .12) \n",
    "\n",
    "plt.title('Sentiment Analysis', fontsize=20)\n",
    "plt.xlabel('<-- Negative -------- Positive -->', fontsize=15)\n",
    "plt.ylabel('<-- Facts -------- Opinions -->', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment of Routine Over Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of looking at the overall sentiment, let's see if there's anything interesting about the sentiment over time throughout each routine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split each routine into 10 parts\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "def split_text(text, n=10):\n",
    "    '''Takes in a string of text and splits into n equal parts, with a default of 10 equal parts.'''\n",
    "\n",
    "    length = len(text) # Calculate length of text\n",
    "    size = math.floor(length / n) # Calculate size of each chunk of text \n",
    "    # Calculate the starting points of each chunk of text\n",
    "    start = np.arange(0, length, size) # numpy.arange([start, ]stop, [step]) ...Return evenly spaced values within a given interval.\n",
    "    \n",
    "    # Pull out equally sized pieces of text and put it into a list. Return a list with chunks of text\n",
    "    split_list = []\n",
    "    for piece in range(n):\n",
    "        split_list.append(text[start[piece]:start[piece]+size])\n",
    "    return split_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transcript</th>\n",
       "      <th>full_name</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ali</th>\n",
       "      <td>Ladies and gentlemen, please welcome to the st...</td>\n",
       "      <td>Ali Wong</td>\n",
       "      <td>0.069359</td>\n",
       "      <td>0.482403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anthony</th>\n",
       "      <td>Thank you. Thank you. Thank you, San Francisco...</td>\n",
       "      <td>Anthony Jeselnik</td>\n",
       "      <td>0.055237</td>\n",
       "      <td>0.558976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill</th>\n",
       "      <td>[cheers and applause] All right, thank you! Th...</td>\n",
       "      <td>Bill Burr</td>\n",
       "      <td>0.016479</td>\n",
       "      <td>0.537016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bo</th>\n",
       "      <td>Bo What? Old MacDonald had a farm E I E I O An...</td>\n",
       "      <td>Bo Burnham</td>\n",
       "      <td>0.074514</td>\n",
       "      <td>0.539368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dave</th>\n",
       "      <td>This is Dave. He tells dirty jokes for a livin...</td>\n",
       "      <td>Dave Chappelle</td>\n",
       "      <td>-0.002690</td>\n",
       "      <td>0.513958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hasan</th>\n",
       "      <td>[theme music: orchestral hip-hop] [crowd roars...</td>\n",
       "      <td>Hasan Minhaj</td>\n",
       "      <td>0.086856</td>\n",
       "      <td>0.460619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jim</th>\n",
       "      <td>[Car horn honks] [Audience cheering] [Announce...</td>\n",
       "      <td>Jim Jefferies</td>\n",
       "      <td>0.044224</td>\n",
       "      <td>0.523382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joe</th>\n",
       "      <td>[rock music playing] [audience cheering] [anno...</td>\n",
       "      <td>Joe Rogan</td>\n",
       "      <td>0.004968</td>\n",
       "      <td>0.551628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>john</th>\n",
       "      <td>All right, Petunia. Wish me luck out there. Yo...</td>\n",
       "      <td>John Mulaney</td>\n",
       "      <td>0.082355</td>\n",
       "      <td>0.484137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>louis</th>\n",
       "      <td>Intro\\nFade the music out. Let’s roll. Hold th...</td>\n",
       "      <td>Louis C.K.</td>\n",
       "      <td>0.056665</td>\n",
       "      <td>0.515796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mike</th>\n",
       "      <td>Wow. Hey, thank you. Thanks. Thank you, guys. ...</td>\n",
       "      <td>Mike Birbiglia</td>\n",
       "      <td>0.092927</td>\n",
       "      <td>0.518476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ricky</th>\n",
       "      <td>Hello. Hello! How you doing? Great. Thank you....</td>\n",
       "      <td>Ricky Gervais</td>\n",
       "      <td>0.066489</td>\n",
       "      <td>0.497313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                transcript         full_name  \\\n",
       "ali      Ladies and gentlemen, please welcome to the st...          Ali Wong   \n",
       "anthony  Thank you. Thank you. Thank you, San Francisco...  Anthony Jeselnik   \n",
       "bill     [cheers and applause] All right, thank you! Th...         Bill Burr   \n",
       "bo       Bo What? Old MacDonald had a farm E I E I O An...        Bo Burnham   \n",
       "dave     This is Dave. He tells dirty jokes for a livin...    Dave Chappelle   \n",
       "hasan    [theme music: orchestral hip-hop] [crowd roars...      Hasan Minhaj   \n",
       "jim      [Car horn honks] [Audience cheering] [Announce...     Jim Jefferies   \n",
       "joe      [rock music playing] [audience cheering] [anno...         Joe Rogan   \n",
       "john     All right, Petunia. Wish me luck out there. Yo...      John Mulaney   \n",
       "louis    Intro\\nFade the music out. Let’s roll. Hold th...        Louis C.K.   \n",
       "mike     Wow. Hey, thank you. Thanks. Thank you, guys. ...    Mike Birbiglia   \n",
       "ricky    Hello. Hello! How you doing? Great. Thank you....     Ricky Gervais   \n",
       "\n",
       "         polarity  subjectivity  \n",
       "ali      0.069359      0.482403  \n",
       "anthony  0.055237      0.558976  \n",
       "bill     0.016479      0.537016  \n",
       "bo       0.074514      0.539368  \n",
       "dave    -0.002690      0.513958  \n",
       "hasan    0.086856      0.460619  \n",
       "jim      0.044224      0.523382  \n",
       "joe      0.004968      0.551628  \n",
       "john     0.082355      0.484137  \n",
       "louis    0.056665      0.515796  \n",
       "mike     0.092927      0.518476  \n",
       "ricky    0.066489      0.497313  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look at our data again\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a list of lists that'll hold all of the pieces of text of all the comedians\n",
    "list_pieces = []\n",
    "for t in data.transcript:\n",
    "    split = split_text(t)\n",
    "    list_pieces.append(split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The list has 12 items, one for each transcript\n",
    "len(list_pieces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And then each transcript has been split into 10 pieces of text\n",
    "len(list_pieces[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.11168482647296207,\n",
       "  0.056407029478458055,\n",
       "  0.09445691155249979,\n",
       "  0.09236886724386723,\n",
       "  -0.014671592775041055,\n",
       "  0.09538361348808912,\n",
       "  0.06079713127248339,\n",
       "  0.08721655328798185,\n",
       "  0.030089690638160044,\n",
       "  0.07351994851994852],\n",
       " [0.13933883477633482,\n",
       "  -0.06333451704545455,\n",
       "  -0.056153799903799935,\n",
       "  0.014602659245516405,\n",
       "  0.16377334420812684,\n",
       "  0.09091338259441709,\n",
       "  0.09420031055900621,\n",
       "  0.11566683919944787,\n",
       "  -0.04238582919138478,\n",
       "  0.058467487373737366],\n",
       " [-0.0326152022580594,\n",
       "  0.006825656825656827,\n",
       "  0.023452001215159095,\n",
       "  0.01934081890331888,\n",
       "  -0.026312183887941466,\n",
       "  0.06207506613756614,\n",
       "  0.030250682288725742,\n",
       "  -0.020351594027441484,\n",
       "  -0.01150485008818343,\n",
       "  0.10757491470108295],\n",
       " [0.17481829573934843,\n",
       "  -0.04116923483102918,\n",
       "  -0.022686011904761886,\n",
       "  0.019912549136687042,\n",
       "  0.0592493946731235,\n",
       "  0.05700242218099361,\n",
       "  0.04407051282051284,\n",
       "  0.11019892033865757,\n",
       "  0.19319944575626394,\n",
       "  0.23029900332225917],\n",
       " [-0.05093449586407334,\n",
       "  -0.05557354333778966,\n",
       "  0.035829891691960644,\n",
       "  0.08313791054959534,\n",
       "  -0.026718682968682954,\n",
       "  0.09785205955660498,\n",
       "  -0.12774488467261902,\n",
       "  -0.0858667847304211,\n",
       "  -0.06019759281122916,\n",
       "  0.15191938178780284],\n",
       " [0.13394056175306174,\n",
       "  0.02376372354497352,\n",
       "  -0.007972552910052912,\n",
       "  0.04265769467158358,\n",
       "  0.07530352418745274,\n",
       "  0.15949924118027567,\n",
       "  0.10203196740128559,\n",
       "  0.14881118326118326,\n",
       "  0.09374107374107374,\n",
       "  0.08352238478820757],\n",
       " [0.09480343501984131,\n",
       "  0.10371625923096511,\n",
       "  0.11308390022675745,\n",
       "  -0.013150758122349044,\n",
       "  -0.02455058908894137,\n",
       "  -0.007404902196568868,\n",
       "  0.17715569885361557,\n",
       "  0.056709288653733085,\n",
       "  -0.021307012256379345,\n",
       "  -0.03465315598380114],\n",
       " [-0.012990848813633627,\n",
       "  -0.016682990620490615,\n",
       "  -0.030701264880952372,\n",
       "  0.020673450261079116,\n",
       "  0.01936133039074215,\n",
       "  -0.005925324675324666,\n",
       "  -0.06152908312447788,\n",
       "  -0.004201252366389975,\n",
       "  0.03232746840701385,\n",
       "  0.08166467395038819],\n",
       " [0.19791810097532989,\n",
       "  -0.008067970389398962,\n",
       "  0.08149321079648951,\n",
       "  0.15441811660561658,\n",
       "  0.13565162907268175,\n",
       "  0.0701145477722339,\n",
       "  -0.019158122579175218,\n",
       "  0.027327034827034836,\n",
       "  0.14517770876466524,\n",
       "  0.050684369412290604],\n",
       " [0.0611880068716006,\n",
       "  0.03561645331664863,\n",
       "  0.1377777723365958,\n",
       "  0.026953933747412007,\n",
       "  0.05593590437340433,\n",
       "  0.029630690660102422,\n",
       "  0.08344959077380953,\n",
       "  0.14363832165641377,\n",
       "  0.022845309452452306,\n",
       "  -0.01568181818181818],\n",
       " [0.1329261237205162,\n",
       "  0.02408706538170825,\n",
       "  0.17029999672045126,\n",
       "  0.039525430617202775,\n",
       "  0.056102175283209765,\n",
       "  0.22768604411461554,\n",
       "  0.04553152114840428,\n",
       "  0.08756809163059162,\n",
       "  0.07564848302190076,\n",
       "  0.06571405688010165],\n",
       " [0.17428735102346216,\n",
       "  0.15246819899189015,\n",
       "  0.05809832528582529,\n",
       "  -0.0330954733672125,\n",
       "  0.16723688497882047,\n",
       "  0.013249771062271064,\n",
       "  0.06146913882762937,\n",
       "  0.023207239808802314,\n",
       "  0.012928669410150898,\n",
       "  0.06295056216931218]]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the polarity for each piece of text\n",
    "\n",
    "polarity_transcript = []\n",
    "for lp in list_pieces:\n",
    "    polarity_piece = []\n",
    "    for p in lp:\n",
    "        polarity_piece.append(TextBlob(p).sentiment.polarity)\n",
    "    polarity_transcript.append(polarity_piece)\n",
    "    \n",
    "polarity_transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the plot for one comedian\n",
    "plt.plot(polarity_transcript[0])\n",
    "plt.title(data['full_name'].index[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nwams/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py:3604: MatplotlibDeprecationWarning: \n",
      "The `ymin` argument was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use `bottom` instead.\n",
      "  alternative='`bottom`', obj_type='argument')\n",
      "/Users/nwams/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py:3610: MatplotlibDeprecationWarning: \n",
      "The `ymax` argument was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use `top` instead.\n",
      "  alternative='`top`', obj_type='argument')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the plot for all comedians\n",
    "plt.rcParams['figure.figsize'] = [16, 12]\n",
    "\n",
    "for index, comedian in enumerate(data.index):    \n",
    "    plt.subplot(3, 4, index+1)\n",
    "    plt.plot(polarity_transcript[index])\n",
    "    plt.plot(np.arange(0,10), np.zeros(10))\n",
    "    plt.title(data['full_name'][index])\n",
    "    plt.ylim(ymin=-.2, ymax=.3)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Ali Wong stays generally positive throughout her routine. Similar comedians are Louis C.K. and Mike Birbiglia.\n",
    "\n",
    "On the other hand, you have some pretty different patterns here like Bo Burnham who gets happier as time passes and Dave Chappelle who has some pretty down moments in his routine."
   ]
  }
 ],
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  "kernelspec": {
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 },
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}