visualize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = []\n",
    "output = []\n",
    "fileName = 'caltech.csv'\n",
    "df = pd.read_csv(fileName, header=None, sep=u\",\", index_col=None)\n",
    "cols = df[1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    0                                                  1\n",
      "0  2/10/2019 20:09:27  I've been fantasizing about my Chem 1b TA a lo...\n",
      "1   2/11/2019 4:16:57  I should really elaborate on this but it would...\n",
      "2  2/11/2019 11:46:07  My PE is my favorite class of the term and als...\n",
      "3  2/11/2019 11:50:03  i've been getting through my classes through s...\n",
      "4   2/21/2019 6:08:28  What's the diff between fundamental rights vs ...\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "len(cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>confession</th>\n",
       "      <th>compound</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutral</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I've been fantasizing about my Chem 1b TA a lo...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2/10/19 20:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I should really elaborate on this but it would...</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.786</td>\n",
       "      <td>2/11/19 4:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>My PE is my favorite class of the term and als...</td>\n",
       "      <td>0.4588</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.875</td>\n",
       "      <td>2/11/19 11:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>i've been getting through my classes through s...</td>\n",
       "      <td>0.7871</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.813</td>\n",
       "      <td>2/11/19 11:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What's the diff between fundamental rights vs ...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2/21/19 6:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          confession  compound  positive  \\\n",
       "0  I've been fantasizing about my Chem 1b TA a lo...    0.0000     0.000   \n",
       "1  I should really elaborate on this but it would...    0.0387     0.109   \n",
       "2  My PE is my favorite class of the term and als...    0.4588     0.125   \n",
       "3  i've been getting through my classes through s...    0.7871     0.151   \n",
       "4  What's the diff between fundamental rights vs ...    0.0000     0.000   \n",
       "\n",
       "   negative  neutral      timestamp  \n",
       "0     0.000    1.000  2/10/19 20:09  \n",
       "1     0.105    0.786   2/11/19 4:16  \n",
       "2     0.000    0.875  2/11/19 11:46  \n",
       "3     0.036    0.813  2/11/19 11:50  \n",
       "4     0.000    1.000   2/21/19 6:08  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fileName = \"out.csv\"\n",
    "df = pd.read_csv(fileName, header=0, sep=u\",\", index_col = None, encoding=\"ISO-8859-1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timestamps = df[\"timestamp\"].tolist()\n",
    "scores = df[\"compound\"].tolist()\n",
    "from scipy.signal import savgol_filter\n",
    "yhat = savgol_filter(scores, 15, 2) # window size 51, polynomial order 3\n",
    "# y = np.sin(x) + np.random.random(100) * 0.2\n",
    "# yhat = savitzky_golay(scores, 51, 3) # window size 51, polynomial order 3\n",
    "plt.plot(timestamps, yhat)\n",
    "plt.title(\"Sentiment score vs. Timestamp\")\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}