2_combine_datasets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining Datasets\n",
    "\n",
    "In this script we will combine the data gathered from the depreessive scrapping script with the data from the mostly non-depressive scripts,\n",
    "analyse their target distribution, standardise their formats and then construct a final dataset containing all of them.\n",
    "\n",
    "The data from the depressive script hsa been manually reviewed and annotated by several different team members, and then reviewwd one more\n",
    "time by aonther member, to ensure consistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "**1. Read in non-depressive dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = pd.read_csv(\"./data/general_tweets.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"./data/tweets_final_1_clean.csv\", engine='python')\n",
    "df2 = pd.read_csv(\"./data/tweets_final_2_clean.csv\", engine='python')\n",
    "df3 = pd.read_csv(\"./data/tweets_final_3_clean.csv\", engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.read_csv(\"./data/tweets_final_4_clean.csv\", engine='python')\n",
    "df5 = pd.read_csv(\"./data/tweets_final_5_clean.csv\", engine='python')\n",
    "df6 = pd.read_csv(\"./data/tweets_final_6_clean.csv\", engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Briefly analyse their content**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Be happy. Be confident. Be kind.\\n\\n #KissablesLoveSMShopmag\\nAllOutDenimFor KISSMARC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@queenjlouise @mamaw_gereck awe thanks 😊</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                   tweet  \\\n",
       "0  Be happy. Be confident. Be kind.\\n\\n #KissablesLoveSMShopmag\\nAllOutDenimFor KISSMARC   \n",
       "1  @queenjlouise @mamaw_gereck awe thanks 😊                                                \n",
       "\n",
       "   target  \n",
       "0  0       \n",
       "1  0       "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Be happy. Be confident. Be kind.\\n\\n #KissablesLoveSMShopmag\\nAllOutDenimFor KISSMARC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@queenjlouise @mamaw_gereck awe thanks 😊</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                   tweet  \\\n",
       "0  Be happy. Be confident. Be kind.\\n\\n #KissablesLoveSMShopmag\\nAllOutDenimFor KISSMARC   \n",
       "1  @queenjlouise @mamaw_gereck awe thanks 😊                                                \n",
       "\n",
       "   target  \n",
       "0  0       \n",
       "1  0       "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mood can be caused by infectious diseases, nutritional deficiencies, neurological conditions, and physiological problems.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>With all of this unnessary  family drama, I feel like moving far away and starting over again. From one thing to another I just feel . Hope I get through this</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                            tweet  \\\n",
       "0   mood can be caused by infectious diseases, nutritional deficiencies, neurological conditions, and physiological problems.                                       \n",
       "1  With all of this unnessary  family drama, I feel like moving far away and starting over again. From one thing to another I just feel . Hope I get through this   \n",
       "\n",
       "   target  \n",
       "0  0       \n",
       "1  1       "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(2)"
   ]
  },
  {
   "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>tweet</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Looking for some beautiful images. Having a really crappy week, and need some support. . Post some great beauty.  Thank you.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>When teens feel down, there are ways they can cope with these feelings to avoid serious , such as make new friends or social connections and participate in sports or school activities. Call Focus &amp; Balance at 210-858-9980 to learn more and see how we can help.  pic.twitter.com/ydrYcTBD2r</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                              tweet  \\\n",
       "0  Looking for some beautiful images. Having a really crappy week, and need some support. . Post some great beauty.  Thank you.                                                                                                                                                                       \n",
       "1  When teens feel down, there are ways they can cope with these feelings to avoid serious , such as make new friends or social connections and participate in sports or school activities. Call Focus & Balance at 210-858-9980 to learn more and see how we can help.  pic.twitter.com/ydrYcTBD2r   \n",
       "\n",
       "   target  \n",
       "0  1       \n",
       "1  0       "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Why is tomorrow Monday???  &amp;  LOL</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I feel like I'm annoying like Always…</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   tweet  target\n",
       "0  Why is tomorrow Monday???  &  LOL      0     \n",
       "1  I feel like I'm annoying like Always…  1     "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016 was the best year so far</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>So over new years think I'm just going to sleep into the new year.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  tweet  \\\n",
       "0  2016 was the best year so far                                          \n",
       "1  So over new years think I'm just going to sleep into the new year.     \n",
       "\n",
       "   target  \n",
       "0  0       \n",
       "1  0       "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>On topic of famous people , apparently stuff needs to be put in place for mental illness. What about non famous people</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tripped hard today! This is promising!</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                     tweet  \\\n",
       "0  On topic of famous people , apparently stuff needs to be put in place for mental illness. What about non famous people    \n",
       "1  Tripped hard today! This is promising!                                                                                    \n",
       "\n",
       "   target  \n",
       "0  0       \n",
       "1  1       "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Why is tomorrow Monday???  &amp;  LOL</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I feel like I'm annoying like Always…</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   tweet  target\n",
       "0  Why is tomorrow Monday???  &  LOL      0     \n",
       "1  I feel like I'm annoying like Always…  1     "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'target distribution - df6')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "ax1 = fig.add_subplot(2,3,1)\n",
    "sns.countplot(x='target', data=df1)\n",
    "ax1.set(title=\"target distribution - df1\")\n",
    "\n",
    "ax2 = fig.add_subplot(2,3,2)\n",
    "sns.countplot(x='target', data=df2)\n",
    "ax2.set(title=\"target distribution - df2\")\n",
    "\n",
    "ax3 = fig.add_subplot(2,3,3)\n",
    "sns.countplot(x='target', data=df3)\n",
    "ax3.set(title=\"target distribution - df3\")\n",
    "\n",
    "ax4 = fig.add_subplot(2,3,4)\n",
    "sns.countplot(x='target', data=df4)\n",
    "ax4.set(title=\"target distribution - df4\")\n",
    "\n",
    "ax5 = fig.add_subplot(2,3,5)\n",
    "sns.countplot(x='target', data=df5)\n",
    "ax5.set(title=\"target distribution - df5\")\n",
    "\n",
    "ax6 = fig.add_subplot(2,3,6)\n",
    "sns.countplot(x='target', data=df6)\n",
    "ax6.set(title=\"target distribution - df6\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,3))\n",
    "plt.title('distribution of non-depressive dataset')\n",
    "ax = sns.barplot(x=df0.target.unique(),y=df0.target.value_counts());\n",
    "ax.set(xlabel='Labels');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Standardise dataset format**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df0[['Tweet','target']].copy()\n",
    "\n",
    "df1 = df1[['tweet_processed','target']].copy()\n",
    "df2 = df2[['tweet_processed','target']].copy()\n",
    "df3 = df3[['tweet_processed','target']].copy()\n",
    "df4 = df4[['tweet_processed','target']].copy()\n",
    "df5 = df5[['tweet_processed','target']].copy()\n",
    "df6 = df6[['tweet_processed','target']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df0.rename(columns = {\"Tweet\": \"tweet\"}) \n",
    "\n",
    "df1 = df1.rename(columns = {\"tweet_processed\": \"tweet\"}) \n",
    "df2 = df2.rename(columns = {\"tweet_processed\": \"tweet\"}) \n",
    "df3 = df3.rename(columns = {\"tweet_processed\": \"tweet\"}) \n",
    "df4 = df4.rename(columns = {\"tweet_processed\": \"tweet\"}) \n",
    "df5 = df5.rename(columns = {\"tweet_processed\": \"tweet\"}) \n",
    "df6 = df6.rename(columns = {\"tweet_processed\": \"tweet\"}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let´s skep df1 and df2 for the time being, as the labels are a little inconsistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = pd.concat([df0, df1, df2, df3, df4, df5, df6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Today in Selfcare: beauty &amp;amp; laughs Kung Fu Panda 3 #Wellness #joy #laughter #selfcare #therapist #philadelphia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I get to spend New Year's home again alone and lonely. ???•</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Depressed and lonely /: Stuck in a deep, never ending hole :(</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>If this is your response to someone saying they're dealing with , you're a terrible person.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apparently you get a free pass just by mentioning    Where was I on the free badge day??!!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>When you will never again give birth to violent men..   pic.twitter.com/pkdPhhlUuZ</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Learning to pretend to have a good time had become a natural skill. I hope one day it is genuine</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aw man im outta pizza rolls</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>When you go out and try to be a part of life &amp; end up feeling like you are less a part of it then when you started.   pic.twitter.com/J625NXrWDb</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>So far he stop texting me…after I said something…so hopefully he doesn't show up at my house…</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                              tweet  \\\n",
       "0  Today in Selfcare: beauty &amp; laughs Kung Fu Panda 3 #Wellness #joy #laughter #selfcare #therapist #philadelphia                                 \n",
       "1  I get to spend New Year's home again alone and lonely. ???•                                                                                        \n",
       "2  Depressed and lonely /: Stuck in a deep, never ending hole :(                                                                                      \n",
       "3  If this is your response to someone saying they're dealing with , you're a terrible person.                                                        \n",
       "4  Apparently you get a free pass just by mentioning    Where was I on the free badge day??!!                                                         \n",
       "5  When you will never again give birth to violent men..   pic.twitter.com/pkdPhhlUuZ                                                                 \n",
       "6  Learning to pretend to have a good time had become a natural skill. I hope one day it is genuine                                                   \n",
       "7  Aw man im outta pizza rolls                                                                                                                        \n",
       "8  When you go out and try to be a part of life & end up feeling like you are less a part of it then when you started.   pic.twitter.com/J625NXrWDb   \n",
       "9  So far he stop texting me…after I said something…so hopefully he doesn't show up at my house…                                                      \n",
       "\n",
       "   target  \n",
       "0  0       \n",
       "1  1       \n",
       "2  1       \n",
       "3  0       \n",
       "4  0       \n",
       "5  0       \n",
       "6  1       \n",
       "7  0       \n",
       "8  0       \n",
       "9  1       "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = df_all.sample(frac=1).reset_index(drop=True)\n",
    "df_all.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,3))\n",
    "plt.title('general (non-depressive) dataset')\n",
    "ax = sns.barplot(x=df_all.target.unique(),y=df_all.target.value_counts());\n",
    "ax.set(xlabel='Labels');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2357\n",
       "1    843 \n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.to_csv(\"./data/tweets_combined.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "Even though the target is not balanced as there are more non-depressive data, we believe it covers a good range of depressive and non-depressive tweets and would be a good basis for developing a depression detection classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}