3-Sentiment-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mic Check / Introduction\n",
    "We are about to perform a sentiment analysis using TextBlob to process our data. What is TextBlob?\n",
    "- [TextBlob](https://textblob.readthedocs.io/en/dev/) is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. [[docs]](https://textblob.readthedocs.io/en/dev/)\n",
    "\n",
    "So, our goal is to label every word in a corpus in terms of polarity and subjectivity. 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "from textblob import TextBlob\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pandas.read_pickle('corpus.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first take a look at the state of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>lyrics</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>biggie</th>\n",
       "      <td>the ten crack commandments what nigga...</td>\n",
       "      <td>The Notorious B.I.G.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eminem</th>\n",
       "      <td>look i was gonna go easy on you not to hurt yo...</td>\n",
       "      <td>Eminem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fifty</th>\n",
       "      <td>man we gotta go get somethin to eat man im hun...</td>\n",
       "      <td>50 Cent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game</th>\n",
       "      <td>im a bldoubleod been on songs with sndoubleop ...</td>\n",
       "      <td>The Game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nas</th>\n",
       "      <td>but thats the whole tragic point my friends wh...</td>\n",
       "      <td>Nas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snoop</th>\n",
       "      <td>ugh hahaha im serious nigga one of yall niggas...</td>\n",
       "      <td>Snoop Doggy Dogg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tupac</th>\n",
       "      <td>how many brothers fell victim to the streets r...</td>\n",
       "      <td>Tupac Amaru</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   lyrics  \\\n",
       "biggie           the ten crack commandments what nigga...   \n",
       "eminem  look i was gonna go easy on you not to hurt yo...   \n",
       "fifty   man we gotta go get somethin to eat man im hun...   \n",
       "game    im a bldoubleod been on songs with sndoubleop ...   \n",
       "nas     but thats the whole tragic point my friends wh...   \n",
       "snoop   ugh hahaha im serious nigga one of yall niggas...   \n",
       "tupac   how many brothers fell victim to the streets r...   \n",
       "\n",
       "                        name  \n",
       "biggie  The Notorious B.I.G.  \n",
       "eminem                Eminem  \n",
       "fifty                50 Cent  \n",
       "game                The Game  \n",
       "nas                      Nas  \n",
       "snoop       Snoop Doggy Dogg  \n",
       "tupac            Tupac Amaru  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next stop: we will create lambda functions to calculate the polarity and subjectivity of each rapper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>lyrics</th>\n",
       "      <th>name</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>biggie</th>\n",
       "      <td>the ten crack commandments what nigga...</td>\n",
       "      <td>The Notorious B.I.G.</td>\n",
       "      <td>0.023304</td>\n",
       "      <td>0.503563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eminem</th>\n",
       "      <td>look i was gonna go easy on you not to hurt yo...</td>\n",
       "      <td>Eminem</td>\n",
       "      <td>-0.064630</td>\n",
       "      <td>0.533969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fifty</th>\n",
       "      <td>man we gotta go get somethin to eat man im hun...</td>\n",
       "      <td>50 Cent</td>\n",
       "      <td>0.037174</td>\n",
       "      <td>0.551940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game</th>\n",
       "      <td>im a bldoubleod been on songs with sndoubleop ...</td>\n",
       "      <td>The Game</td>\n",
       "      <td>0.003092</td>\n",
       "      <td>0.436697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nas</th>\n",
       "      <td>but thats the whole tragic point my friends wh...</td>\n",
       "      <td>Nas</td>\n",
       "      <td>-0.010095</td>\n",
       "      <td>0.453374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snoop</th>\n",
       "      <td>ugh hahaha im serious nigga one of yall niggas...</td>\n",
       "      <td>Snoop Doggy Dogg</td>\n",
       "      <td>-0.020491</td>\n",
       "      <td>0.481471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tupac</th>\n",
       "      <td>how many brothers fell victim to the streets r...</td>\n",
       "      <td>Tupac Amaru</td>\n",
       "      <td>0.058860</td>\n",
       "      <td>0.442701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   lyrics  \\\n",
       "biggie           the ten crack commandments what nigga...   \n",
       "eminem  look i was gonna go easy on you not to hurt yo...   \n",
       "fifty   man we gotta go get somethin to eat man im hun...   \n",
       "game    im a bldoubleod been on songs with sndoubleop ...   \n",
       "nas     but thats the whole tragic point my friends wh...   \n",
       "snoop   ugh hahaha im serious nigga one of yall niggas...   \n",
       "tupac   how many brothers fell victim to the streets r...   \n",
       "\n",
       "                        name  polarity  subjectivity  \n",
       "biggie  The Notorious B.I.G.  0.023304      0.503563  \n",
       "eminem                Eminem -0.064630      0.533969  \n",
       "fifty                50 Cent  0.037174      0.551940  \n",
       "game                The Game  0.003092      0.436697  \n",
       "nas                      Nas -0.010095      0.453374  \n",
       "snoop       Snoop Doggy Dogg -0.020491      0.481471  \n",
       "tupac            Tupac Amaru  0.058860      0.442701  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pol = lambda x: TextBlob(x).sentiment.polarity\n",
    "sub = lambda x: TextBlob(x).sentiment.subjectivity\n",
    "\n",
    "data['polarity'] = data['lyrics'].apply(pol)\n",
    "data['subjectivity'] = data['lyrics'].apply(sub)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well that's something. We now know that Tupac is the most positive and he the least subjective in his lyrics, while 50 Cent is most subjective.\n",
    "\n",
    "But, how does TextBlob know what words mean? Well, lingvists have given different labels to all the words in the English vocabulary and TextBlob is a library that wraps that knowledge. Let's see an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=0.7, subjectivity=0.6000000000000001)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextBlob('good').sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=0.9099999999999999, subjectivity=0.7800000000000001)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextBlob('very good').sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=1.0, subjectivity=1.0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextBlob('excellent').sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output says that the word 'nice' transmits 60% of the positivity a word can transmit, while it's 100% of subjective nature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "plt.rcParams['figure.figsize'] = [10, 8]\n",
    "\n",
    "for index, rapper in enumerate(data.index):\n",
    "    x = data.polarity.loc[rapper]\n",
    "    y = data.subjectivity.loc[rapper]\n",
    "    plt.scatter(x, y, color='blue')\n",
    "    plt.text(x+.001, y+.001, data['name'][index], fontsize=10)\n",
    "    plt.xlim(-.07, .07) \n",
    "    \n",
    "plt.title('Sentiment Analysis', fontsize=20)\n",
    "plt.xlabel('Positivity', fontsize=15)\n",
    "plt.ylabel('Objectivity', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusions from the sentiment analysis:\n",
    "- Tupac is the most 'positive' in his words, while Eminem is by far the most 'negative' lyricist \n",
    "- The Game, Nas and Tupac are mostly subjective and talk about their feelings, while Eminem and 50 Cent state more facts."
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}