Emoji-EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emoji Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_txt_raw = open('data/us_train.text','r',encoding=\"utf8\")\n",
    "train_labels_raw = open('data/us_train.labels','r',encoding=\"utf8\")\n",
    "\n",
    "train_txt = []\n",
    "train_labels = []\n",
    "\n",
    "for sentence in train_txt_raw:\n",
    "    sentence = sentence.rstrip()\n",
    "    train_txt.append(sentence)\n",
    "    \n",
    "for sentence in train_labels_raw:\n",
    "    sentence = sentence.rstrip()\n",
    "    sentence = int(sentence)\n",
    "    train_labels.append(sentence)\n",
    "\n",
    "\n",
    "df = pd.DataFrame()\n",
    "df[\"Tweet\"] = train_txt\n",
    "df[\"Label\"] = train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Tweet</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A little throwback with my favourite person @ ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>glam on @user yesterday for #kcon makeup using...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Democracy Plaza in the wake of a stunning outc...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Then &amp;amp; Now. VILO @ Walt Disney Magic Kingdom</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Who never... @ A Galaxy Far Far Away</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dinner in FLA tonight // Pan-seared salmon ove...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>It's my fav seniors last game congrats on beat...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I got to to go formal with my best friend @ Ph...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>'Cause I Miss My Little Homies .#Throwback #Co...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Birthday Kisses @ Madison, Wisconsin</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweet  Label\n",
       "0  A little throwback with my favourite person @ ...      0\n",
       "1  glam on @user yesterday for #kcon makeup using...      7\n",
       "2  Democracy Plaza in the wake of a stunning outc...     11\n",
       "3   Then &amp; Now. VILO @ Walt Disney Magic Kingdom      0\n",
       "4               Who never... @ A Galaxy Far Far Away      2\n",
       "5  Dinner in FLA tonight // Pan-seared salmon ove...      1\n",
       "6  It's my fav seniors last game congrats on beat...      8\n",
       "7  I got to to go formal with my best friend @ Ph...      0\n",
       "8  'Cause I Miss My Little Homies .#Throwback #Co...     13\n",
       "9               Birthday Kisses @ Madison, Wisconsin      9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Emoji mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t❤️\t Red heart\t\n",
      "\n",
      "1\t😍\t Smiling face with hearteyes\t\n",
      "\n",
      "2\t😂\t Face with tears of joy\t\n",
      "\n",
      "3\t💕\t Two hearts\n",
      "\n",
      "4\t🔥\t Fire\n",
      "\n",
      "5\t😊\t Smiling face with smiling eyes\t\n",
      "\n",
      "6\t😎\t Smiling face with sunglasses\t\n",
      "\n",
      "7\t✨\t Sparkles \t\n",
      "\n",
      "8\t💙\t Blue heart\n",
      "\n",
      "9\t😘\t Face blowing a kiss\t\n",
      "\n",
      "10\t📷\t Camera\t\n",
      "\n",
      "11\t🇺🇸\t United States\t\n",
      "\n",
      "12\t☀️\t Sun\n",
      "\n",
      "13\t💜\t Purple heart \t\n",
      "\n",
      "14\t😉\t Winking face\t\n",
      "\n",
      "15\t💯\t Hundred points\t\n",
      "\n",
      "16\t😁\t Beaming face with smiling eyes\t\n",
      "\n",
      "17\t🎄\tChristmas tree\t\n",
      "\n",
      "18\t📸\tCamera with flash\n",
      "\n",
      "19\t😜\tWinking face with tongue\t\n",
      "\n"
     ]
    }
   ],
   "source": [
    "emoji_raw = open('data/us_mapping.txt','r',encoding=\"utf8\")\n",
    "\n",
    "emojis=[]\n",
    "for sentence in emoji_raw:\n",
    "    print(sentence)\n",
    "    sentence = sentence.rstrip()\n",
    "    emojis.append(sentence)\n",
    "\n",
    "mapp = []\n",
    "emos = []\n",
    "sent = []\n",
    "\n",
    "for e in emojis:\n",
    "    mapp.append(e.split()[0])\n",
    "    emos.append(e.split()[1])\n",
    "    sent.append(\" \".join(e.split()[2:]))\n",
    "\n",
    "df_em = pd.DataFrame({'Label':mapp})\n",
    "df_em[\"Emoji\"] = emos\n",
    "df_em[\"Description\"] = sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.countplot(x=\"Label\", data=df, palette=\"plasma\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "df[\"Length\"] = df[\"Tweet\"].apply(len)\n",
    "sns.distplot(df[\"Length\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Label</th>\n",
       "      <th>Length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.903680</td>\n",
       "      <td>66.645380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.851024</td>\n",
       "      <td>22.514753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>146.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Label        Length\n",
       "count  50000.000000  50000.000000\n",
       "mean       5.903680     66.645380\n",
       "std        5.851024     22.514753\n",
       "min        0.000000      4.000000\n",
       "25%        1.000000     50.000000\n",
       "50%        4.000000     67.000000\n",
       "75%       10.000000     87.000000\n",
       "max       19.000000    146.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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