view_smoothed_scores.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View all of the smoothed college confessions sentiment scores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Unnamed: 0</th>\n",
       "      <th>confession</th>\n",
       "      <th>compound</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>compound_smoothed</th>\n",
       "      <th>positive_smoothed</th>\n",
       "      <th>negative_smoothed</th>\n",
       "      <th>neutral_smoothed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>\"My roommate is a double major in CS and Econ...</td>\n",
       "      <td>0.2235</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.910</td>\n",
       "      <td>02/28/2019 21:30:00</td>\n",
       "      <td>0.815285</td>\n",
       "      <td>0.158606</td>\n",
       "      <td>0.024788</td>\n",
       "      <td>0.816444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>\"Alright, so IÃ¢ÂÂm not even a Haas major, ...</td>\n",
       "      <td>0.8247</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.749</td>\n",
       "      <td>02/28/2019 20:00:00</td>\n",
       "      <td>0.636349</td>\n",
       "      <td>0.142689</td>\n",
       "      <td>0.042543</td>\n",
       "      <td>0.814697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>\"It makes me so sad when people are rude to p...</td>\n",
       "      <td>0.8076</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.741</td>\n",
       "      <td>02/28/2019 18:30:00</td>\n",
       "      <td>0.481399</td>\n",
       "      <td>0.129731</td>\n",
       "      <td>0.057966</td>\n",
       "      <td>0.812309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>\"Okay, gal here. So IÃ¢ÂÂve been in an open...</td>\n",
       "      <td>-0.0147</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.865</td>\n",
       "      <td>02/28/2019 17:00:00</td>\n",
       "      <td>0.350433</td>\n",
       "      <td>0.119731</td>\n",
       "      <td>0.071057</td>\n",
       "      <td>0.809282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>\"Looking for Berkeley girls on Tinder (as a g...</td>\n",
       "      <td>0.3818</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.837</td>\n",
       "      <td>02/28/2019 15:30:00</td>\n",
       "      <td>0.243452</td>\n",
       "      <td>0.112690</td>\n",
       "      <td>0.081816</td>\n",
       "      <td>0.805615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                         confession  compound  \\\n",
       "0           0   \"My roommate is a double major in CS and Econ...    0.2235   \n",
       "1           1   \"Alright, so IÃ¢ÂÂm not even a Haas major, ...    0.8247   \n",
       "2           2   \"It makes me so sad when people are rude to p...    0.8076   \n",
       "3           3   \"Okay, gal here. So IÃ¢ÂÂve been in an open...   -0.0147   \n",
       "4           4   \"Looking for Berkeley girls on Tinder (as a g...    0.3818   \n",
       "\n",
       "   positive  negative  neutral            timestamp  compound_smoothed  \\\n",
       "0     0.090     0.000    0.910  02/28/2019 21:30:00           0.815285   \n",
       "1     0.161     0.090    0.749  02/28/2019 20:00:00           0.636349   \n",
       "2     0.156     0.102    0.741  02/28/2019 18:30:00           0.481399   \n",
       "3     0.079     0.057    0.865  02/28/2019 17:00:00           0.350433   \n",
       "4     0.112     0.051    0.837  02/28/2019 15:30:00           0.243452   \n",
       "\n",
       "   positive_smoothed  negative_smoothed  neutral_smoothed  \n",
       "0           0.158606           0.024788          0.816444  \n",
       "1           0.142689           0.042543          0.814697  \n",
       "2           0.129731           0.057966          0.812309  \n",
       "3           0.119731           0.071057          0.809282  \n",
       "4           0.112690           0.081816          0.805615  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caltech_smoothed = \"berkout_smoothed.csv\"\n",
    "df = pd.read_csv(caltech_smoothed, header=0, sep=u\",\", index_col = None, encoding=\"ISO-8859-1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_metric(score_name, df):\n",
    "    \"\"\"\n",
    "    Helper function to plot a smoothed metric over time \n",
    "    \"\"\"\n",
    "    timestamps = df[\"timestamp\"].tolist()\n",
    "    scores = df[score_name].tolist()\n",
    "    plt.plot(timestamps, scores)\n",
    "    plt.title(\"Sentiment \" + score_name + \" score vs. Timestamp\")\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(\"compound_smoothed\", df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(\"positive_smoothed\", df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(\"negative_smoothed\", df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(\"neutral_smoothed\", df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "caltech_smoothed = \"caltech_smoothed.csv\"\n",
    "caltech = pd.read_csv(caltech_smoothed, header=0, sep=u\",\", index_col = None, encoding=\"ISO-8859-1\").dropna()\n",
    "berkeley_smoothed = \"berkout_smoothed.csv\"\n",
    "berkeley = pd.read_csv(berkeley_smoothed, header=0, sep=u\",\", index_col = None, encoding=\"ISO-8859-1\").dropna()\n",
    "mit_smoothed = \"mitout_smoothed.csv\"\n",
    "mit = pd.read_csv(mit_smoothed, header=0, sep=u\",\", index_col = None, encoding=\"ISO-8859-1\").dropna()\n",
    "uci_smoothed = \"uciout_smoothed.csv\"\n",
    "uci = pd.read_csv(uci_smoothed, header=0, sep=u\",\", index_col = None, encoding=\"ISO-8859-1\").dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Caltech confessions size: 198\n",
      "Berkeley confessions size: 87\n",
      "MIT confessions size: 178\n",
      "UCI confessions size: 105\n"
     ]
    }
   ],
   "source": [
    "caltech_compound = caltech[\"compound_smoothed\"].tolist()\n",
    "berkeley_compound = berkeley[\"compound_smoothed\"].tolist()\n",
    "mit_compound = mit[\"compound_smoothed\"].tolist()\n",
    "uci_compound = uci[\"compound_smoothed\"].tolist()\n",
    "print(\"Caltech confessions size: \" + str(len(caltech_compound)))\n",
    "print(\"Berkeley confessions size: \" + str(len(berkeley_compound)))\n",
    "print(\"MIT confessions size: \" + str(len(mit_compound)))\n",
    "print(\"UCI confessions size: \" + str(len(uci_compound)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep track of the indices to mark data points\n",
    "caltech_range = len(caltech_compound)\n",
    "berkeley_range = len(berkeley_compound)\n",
    "mit_range = len(mit_compound)\n",
    "uci_range = len(uci_compound)\n",
    "\n",
    "tMax = min(caltech_range, berkeley_range, mit_range, uci_range)\n",
    "t = np.arange(tMax)\n",
    "dataset = []\n",
    "for i in range(tMax):\n",
    "    \n",
    "    # Form the datapoint to be put into the html file \n",
    "    pointDict = {\"x\": i}\n",
    "    pointDict[\"val_0\"] = caltech_compound[i]\n",
    "    pointDict[\"val_1\"] = berkeley_compound[i]\n",
    "    pointDict[\"val_2\"] = mit_compound[i]\n",
    "    pointDict[\"val_3\"] = uci_compound[i]\n",
    "    \n",
    "    dataset.append(pointDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now create 4 different datasets for the points \n",
    "# Helper function to aggregate \n",
    "def createDataset(df):\n",
    "    \n",
    "    # Sort the entries by timestamp \n",
    "    df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
    "    df.sort_values(by=['timestamp'], inplace=True)\n",
    "    \n",
    "    timestamps = df[\"timestamp\"].tolist()\n",
    "    scores = df[\"compound_smoothed\"].tolist()\n",
    "    \n",
    "    # Combine all scores for the same date timestamp \n",
    "#     real_times = []\n",
    "#     real_scores = []\n",
    "#     for i in range(len(timestamps)):\n",
    "#         aTime = timestamps[i]\n",
    "#         score = scores[i]\n",
    "        \n",
    "#         # Split time into date and time \n",
    "#         date = str(aTime.date())\n",
    "        \n",
    "#         if date in real_times:\n",
    "#             # Add the score to that date \n",
    "#             j = real_times.index(date)\n",
    "#             real_scores[j] += score \n",
    "#         else:\n",
    "#             # Create a new entry \n",
    "#             real_times.append(date)\n",
    "#             real_scores.append(score)\n",
    "    \n",
    "    # Build the dataset of points \n",
    "    tMax = len(timestamps)\n",
    "    t = np.arange(tMax)\n",
    "    dataset = []\n",
    "    minTime = timestamps[0].timestamp()\n",
    "    for i in range(tMax):\n",
    "\n",
    "        # Form the datapoint to be put into the html file \n",
    "        pointDict = {\"x\": str(timestamps[i].timestamp() - minTime)}\n",
    "        pointDict[\"val_0\"] = scores[i]\n",
    "\n",
    "        dataset.append(pointDict)\n",
    "    return dataset\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'val_0': -0.20134205882352774, 'x': '0.0'},\n",
       " {'val_0': -0.18401303167420907, 'x': '80340.0'},\n",
       " {'val_0': -0.14241411764705947, 'x': '84120.0'},\n",
       " {'val_0': -0.0908533031674212, 'x': '84120.0'},\n",
       " {'val_0': -0.03519927601809975, 'x': '84540.0'},\n",
       " {'val_0': 0.09605321266968364, 'x': '94620.0'},\n",
       " {'val_0': 0.16189665158371108, 'x': '114540.0'},\n",
       " {'val_0': 0.18652790239172667, 'x': '126600.0'},\n",
       " {'val_0': 0.1856516548157734, 'x': '133260.0'},\n",
       " {'val_0': 0.15926790885585096, 'x': '146640.0'},\n",
       " {'val_0': 0.10737666451195982, 'x': '168840.0'},\n",
       " {'val_0': 0.029977921784099526, 'x': '194520.0'},\n",
       " {'val_0': -0.07292831932772947, 'x': '199800.0'},\n",
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       " {'val_0': -0.03614027149321288, 'x': '6267900.0'}]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "createDataset(caltech)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
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       " {'val_0': 0.5562123529411763, 'x': '245400.0'}]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "createDataset(mit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
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