News_Analysis_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis: News Titles & Headlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goals\n",
    "\n",
    "* Exploration of sentiment\n",
    "* Exploration of popularity\n",
    "* Exploration of topics favored by sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_data = pd.read_csv('News_Final.csv', encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>IDLink</th>\n",
       "      <th>Title</th>\n",
       "      <th>Headline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Topic</th>\n",
       "      <th>PublishDate</th>\n",
       "      <th>SentimentTitle</th>\n",
       "      <th>SentimentHeadline</th>\n",
       "      <th>Facebook</th>\n",
       "      <th>GooglePlus</th>\n",
       "      <th>LinkedIn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99248.0</td>\n",
       "      <td>Obama Lays Wreath at Arlington National Cemetery</td>\n",
       "      <td>Obama Lays Wreath at Arlington National Cemete...</td>\n",
       "      <td>USA TODAY</td>\n",
       "      <td>obama</td>\n",
       "      <td>2002-04-02 00:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.053300</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10423.0</td>\n",
       "      <td>A Look at the Health of the Chinese Economy</td>\n",
       "      <td>Tim Haywood, investment director business-unit...</td>\n",
       "      <td>Bloomberg</td>\n",
       "      <td>economy</td>\n",
       "      <td>2008-09-20 00:00:00</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>-0.156386</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18828.0</td>\n",
       "      <td>Nouriel Roubini: Global Economy Not Back to 2008</td>\n",
       "      <td>Nouriel Roubini, NYU professor and chairman at...</td>\n",
       "      <td>Bloomberg</td>\n",
       "      <td>economy</td>\n",
       "      <td>2012-01-28 00:00:00</td>\n",
       "      <td>-0.425210</td>\n",
       "      <td>0.139754</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27788.0</td>\n",
       "      <td>Finland GDP Expands In Q4</td>\n",
       "      <td>Finland's economy expanded marginally in the t...</td>\n",
       "      <td>RTT News</td>\n",
       "      <td>economy</td>\n",
       "      <td>2015-03-01 00:06:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026064</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27789.0</td>\n",
       "      <td>Tourism, govt spending buoys Thai economy in J...</td>\n",
       "      <td>Tourism and public spending continued to boost...</td>\n",
       "      <td>The Nation - Thailand&amp;#39;s English news</td>\n",
       "      <td>economy</td>\n",
       "      <td>2015-03-01 00:11:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.141084</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    IDLink                                              Title  \\\n",
       "0  99248.0   Obama Lays Wreath at Arlington National Cemetery   \n",
       "1  10423.0        A Look at the Health of the Chinese Economy   \n",
       "2  18828.0   Nouriel Roubini: Global Economy Not Back to 2008   \n",
       "3  27788.0                          Finland GDP Expands In Q4   \n",
       "4  27789.0  Tourism, govt spending buoys Thai economy in J...   \n",
       "\n",
       "                                            Headline  \\\n",
       "0  Obama Lays Wreath at Arlington National Cemete...   \n",
       "1  Tim Haywood, investment director business-unit...   \n",
       "2  Nouriel Roubini, NYU professor and chairman at...   \n",
       "3  Finland's economy expanded marginally in the t...   \n",
       "4  Tourism and public spending continued to boost...   \n",
       "\n",
       "                                     Source    Topic          PublishDate  \\\n",
       "0                                 USA TODAY    obama  2002-04-02 00:00:00   \n",
       "1                                 Bloomberg  economy  2008-09-20 00:00:00   \n",
       "2                                 Bloomberg  economy  2012-01-28 00:00:00   \n",
       "3                                  RTT News  economy  2015-03-01 00:06:00   \n",
       "4  The Nation - Thailand&#39;s English news  economy  2015-03-01 00:11:00   \n",
       "\n",
       "   SentimentTitle  SentimentHeadline  Facebook  GooglePlus  LinkedIn  \n",
       "0        0.000000          -0.053300        -1          -1        -1  \n",
       "1        0.208333          -0.156386        -1          -1        -1  \n",
       "2       -0.425210           0.139754        -1          -1        -1  \n",
       "3        0.000000           0.026064        -1          -1        -1  \n",
       "4        0.000000           0.141084        -1          -1        -1  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['IDLink', 'Title', 'Headline', 'Source', 'Topic', 'PublishDate',\n",
       "       'SentimentTitle', 'SentimentHeadline', 'Facebook', 'GooglePlus',\n",
       "       'LinkedIn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vL4MHDyYhIeGZ+1y5ciW1atUyrn/55ReOHz/+0Lr08Nf+/27Xrl14eHjw/fffp6oLDg6mf//+6RqLiIiIiIiIiIhkf0pMZpKpU6cSFRXF9u3bmT17NgcPHuSzzz575v4CAwNZvXq1cd2lSxcuXbqUHqE+VFr6nzJlCpcvX86wGERERERERERE5J9DiclM4uTkhMlkolChQlSsWJHOnTuzZs2aZ+4vd+7cuLi4pGOEz8/R0ZHx48dndRgiIiIiIiIiIpINKDGZRf6eVOzUqRMhISE0aNAAf39/rly5woULF/jwww+pWLEiderUYeLEiSQmJgKWy7UDAgIA6Nq1K9OnTzf6nDJlCpUqVcLPz4+wsDCjPCkpifHjx1OrVi3Kly9P3bp1LZZhBwQEMGHCBPz8/AgMDDSe8/f+/27IkCGEh4ezZ8+eR96zd+9eWrZsiZeXF02aNDHe+ty4cSO+vr6kpKQAcOTIETw8PNi4caPRtlmzZvzwww8kJCTQp08fqlatSqVKlejduzfx8fGP+bVFRERERERERORFo8RkFrhy5QoLFy6kefPmFuUrV65k3LhxzJw5k/z589OrVy+cnJxYsWIFEydOZNu2bUyePDlVf8uXLwfuLxfv1q0bABcvXuTo0aMsXbqUfv36MXLkSKKjowGYM2cOW7Zs4YsvvmDdunW89dZbjB49mosXLxp9/vDDD8ydO5dJkyYZycO/9v8wtWvXpn79+owcOZKkpKRU9fHx8XTv3p1mzZoRERFBr169GD16NFu2bKF69eokJCRw7NgxAHbv3o2VlRW//vqr0TYmJgY/Pz+mTp3KuXPnWLhwIcuWLePy5cuMGzcuzb+/iIiIiIiIiIhkvVxZHUBO0aNHD2xsbDCbzdy+fRtnZ2eGDh1qcU+tWrWoUqUKADt37iQuLo5ly5ZhY2MDwL///W+6deuW6jCZB29fOjk5kS9fPgBsbW0ZN24cLi4ulC5dmt27d7NkyRJq1KjBa6+9xpgxY6hYsaIR25dffsmpU6coVKgQcP/txDJlylg856/9P8rQoUMJDAxk/vz5vP/++xZ1oaGh+Pr60qVLFwDc3Nw4efIkCxYsICAgAG9vb3bt2kXZsmXZs2cPtWrVMhKT0dHRlCtXDhcXF86dO0fevHkpVqwY+fLlY8KECVy/fj0NfwUREREREREREXlRKDGZSUJCQvDx8QHg2rVrRERE0KZNG8LCwnB3dwfA1dXVuD8mJobr168biUoAs9lMUlIS58+ff+LzihUrZrFcvFy5cixZsgSA+vXrs2PHDj777DNOnjzJ4cOHAYxl1H+P5WkUKVKEnj17MnPmTJo2bWpRd/LkSX7++WfjdwBITk424vTz82P37t106dKFvXv3Mm3aNN59913u3LlDdHS0saS8e/fudO/enerVq+Pr60uDBg1o0aLFM8UrIiIiIiIiIiJZQ4nJTFKwYEHc3NyMay8vL7Zv386yZcsYOHAgAHZ2dkZ9cnIybm5uzJ49O1VfhQsXfuLzrK0tV+mnpKRga2sL3N97cunSpbzzzju8+eabDB8+3Nin8gF7e/u0D+5vunbtSnh4OGPGjMHBwcEoT05OpkmTJnz44YcPjdXPz4/58+dz7Ngx8uTJg6+vLy4uLhw4cIDo6Gi++OILAHx8fNi6dStbtmzhp59+4rPPPiMiIoKFCxc+c8wiIiIiIiIiIpK5tMdkFrt3795Dy93d3blw4QLOzs64ubnh5uZGfHw8kyZNwmw2P7HfuLg4bt68aVwfOHCAUqVKAbBkyRKGDh3Kp59+SpMmTbh9+zZAmvpNC1tbW4YPH87GjRvZvXu3xZhiY2ON8bi5uREVFWXskVmhQgWsra0JDQ2lcuXKAFSpUoUlS5aQmJiIl5cXAPPnz+e3336jefPmTJo0ia+//prdu3dz6dKldIlfREREREREREQynhKTmeTatWvEx8cTHx/P2bNnmTp1KqdPn6ZRo0YPvd/Pz4/ixYvTv39/jh49yv79+xk6dCjW1tYPfZsxb968/P7779y4cQO4f/J2cHAwx48fZ8mSJaxfv56goCAAnJ2d2bp1K2fPnmXfvn0MGDAAwDjx+2H+3v+TVK1alebNm3Pu3DmjrH379hw5coRJkyYRGxvLunXr+Pzzz419La2tralRowarVq0ylrBXqVKFNWvWUKNGDWOvzQsXLjBq1Ch+/fVXzp49S0REBEWLFiV//vxpik1ERERERERERLKelnJnkj59+hjf7e3tKVOmDNOnT6dSpUoPvd/GxoaZM2cyZswY2rZti729PQ0aNCA4OPih9wcFBTFp0iTOnz+Ph4cHZcuWpWjRorRp0wZnZ2fGjh2Lp6cnAGPHjmXEiBE0adKEggUL0rp1a2xtbTl8+DB169Z9Yv+DBg1K05iDg4PZtm2bce3q6srs2bOZOHEi8+bNw2Qy8dFHH9G+fXvjHj8/P3788UcjMfn6669jNpvx9/c37vnkk09ISEigV69e3Lx5E29vb2bNmmUkLkVERERERERE5MVnZU6v9bsi2cj7a7byv1u3szoMERF5RuEtA4mPT9tb/PJsTCZH/caSoTTHJDNonklG0xyTzJAd5pm1tRUFCjg8+ca/t8uAWEREREREREREREQeS4lJERERERERERERyXTaY1JypDmBD99LU0REsoc7SclZHYKIiIiIiDwnJSYlR7p8OYGUFG2vKhkjO+z/Idmf5pmIiIiIiGR3WsotIiIiIiIiIiIimU6JSREREREREREREcl0WsotOdKzHGEv8jRMJsesDkFygKyaZ3eSkrlx9XaWPFtERERERP45lJiUHKn72j3E37qb1WGIiGRLq97xQ7tbioiIiIjI89JSbhEREREREREREcl0SkyKiIiIiIiIiIhIpntiYjIgIAAPDw/jU6ZMGapWrUrPnj35448/0iUIs9nM4sWLSUlJSZf+ntXKlSupVavWU7c7fPgw5cqVIzQ0NFXdgQMHKFOmDBs2bHhiP/379yc4OPipn58W169fx8PDg0WLFj20PiwszOLv/NfPpk2bOH36NB4eHsTFxWVIfCIiIiIiIiIikrOkaY/J4OBgmjZtCkBKSgonTpxg+PDhDBw4kO++++65g9izZw8jRoygVatWWFtn3UucgYGB1KlT56nblStXjnbt2jFt2jQCAwPJnz8/cD/hGhISQp06dXjjjTfSOdr0V7hwYZYvX56q3MnJCRsbG6KionBxccmCyERERERERERE5J8mTVlABwcHTCYTJpOJQoUKUbNmTT7++GN27drFjRvPv/292Wx+7j7SQ+7cuZ858danTx/s7OyYMmWKURYWFkZMTAzDhw9PrxCfmY2NjcW/j7rnwd/5rx87Ozuj7nHtRURERERERERE0uqZX0+0s7O738H/f8Pxxo0bDBw4kMqVK1OzZk2GDRtGQkICALt27cLDw4Pk5GSjfXBwMP379ycuLo7OnTsDUL58eXbt2gXA0qVLqVevHj4+PrRr144DBw4YbQMCApgwYQJ+fn4EBgaSnJzMtGnT8Pf3x9PTkzZt2rB///5UMaekpODv709YWJhFeWBgIKGhoamWcm/dupW33noLT09PKleuTJ8+fYwx/Z2joyMDBgwgLCyMI0eOkJCQwJQpU+jbty9FihQx7gsLC6NRo0ZUqFABX19fRo4cyb1794z6hIQE+vbti7e3N3Xq1CEiIsKou3PnDuPHj6dWrVpUrFiRHj16WCyn/+OPP/j444+pWrUqvr6+jB49msTERADy5s2LjY0Njo6OD43/Sf66lDs5ORkPDw/Cw8Np2rQpPj4+dOnShbNnzxr3b968mRYtWuDp6UmVKlX417/+xc2bNwGYMmUK/fr1Y/jw4VSqVImAgADmzJljtE1OTmbKlCn4+flRqVIlevXqRXx8PHD/bzhjxgz8/PyoXLky77//PmfOnHmmMYmIiIiIiIiISNZ5psRkbGwsX3zxBf7+/uTLlw+AwYMH8+effxIaGsrs2bM5deoUgwYNemJfRYoUYfr06QBs374dHx8ftmzZwrRp0xg0aBCrVq2iVq1adOnShf/9739Gux9++IG5c+cyadIktm7dSmhoKBMnTmTNmjWUK1eOjz/+ONWeldbW1jRq1Ij169cbZcePHyc2NpaGDRta3Hv27Fk++ugj2rZty9q1a5k2bRq//PILixcvfuRYmjdvTpUqVZg4cSJfffUVrq6udOzY0ajftWsXY8aMoX///qxbt47hw4cTFhbG5s2bjXs2btxI+fLliYiIoGHDhgwePNh4K3Xo0KFs2bKFzz//nCVLlpCYmMiHH35ISkoKiYmJdO7cmcTERBYuXMjUqVPZsmULEyZMAMDKygpHR0deeumlJ/5N0urLL79k6NChLFiwgAsXLjB16lTgfhLzk08+oUOHDqxdu5YpU6awY8cOli1bZrRdv349efPmZcWKFbzzzjtMnDiRkydPAjBt2jRWrlzJ2LFjCQsL4+bNm8Zc+u6774iMjGTixIksW7aMokWLEhQUxN27d9NtXCIiIiIiIiIikvHStMdkSEgIY8eOBe6/zWZra0u9evUYPHgwAGfOnGHjxo388ssvODs7AzB+/HgCAgKeeECOjY0NTk5OABQoUIBcuXIxd+5cunfvTv369QHo2bMn0dHRhIWF0atXLwCaNWtGmTJlgPsJv1y5clG0aFGKFy9Ov379eOONN0hJSUm1Z2XTpk3p0KEDN27cwNHRkXXr1uHr68vLL79scd+9e/cYMmQIbdq0AaBYsWLUqFGDEydOPHY8I0aM4M0332TPnj0sXbrU4vl58uRhzJgxxriKFSvGN998w++//27sQenj48N7770HQI8ePZg/fz4nT56kePHiREZG8s033+Dr6wvAxIkTqVOnDjt27OD27dtcunSJFStWGMnHYcOG0bt3b/r27Uu+fPmemJg8f/48Pj4+FmVt2rR55IE8QUFBVKtWDYC2bduyZMkS47cbNmwYrVq1Msbp6+vL77//brR1dnbm008/xdraml69evHtt99y6NAhSpQowbJlyxg4cKDx9uqIESNYtWoVZrOZuXPnEhISYjx3+PDh1KtXj40bNxr7oIqIiIiIiIiIyIsvTYnJ3r1706hRI27dusWMGTM4f/48ffv2NQ55iYmJwWw2U7du3VRtY2Njn/pAm5iYGCZPnsy0adOMssTERAoXLmxcu7q6Gt+bNWvGypUradCgAZ6engQEBNCyZUty5Uo9PG9vbwoVKsSWLVt48803WbduHV27dk11X4kSJbCzs+Orr77i999/5/fff+fEiRM0adLksbGXKlWK+vXrc+/ePcqWLWtR5+XlRe7cuZk2bRoxMTEcO3aM06dPWxy4U7x4ceP7g2XXd+/e5dSpU5jNZry9vY16FxcX3NzciImJ4fbt25QoUcIi8ejj40NycjJnzpyhbNmyvPTSS49dyl2wYEEWLlxoUfa4+1955RXju4ODA0lJSQCULFkSe3v7VL/dm2++adxfrFgxi3mRL18+kpOTuXTpElevXqV8+fJGXYkSJejbty/Xr18nPj6evn37WrS9c+cOsbGxj4xTRERERERERERePGlKTD5IgMH9/QFbtmxJr169WLZsGba2tty7d4+8efOyevXqVG1NJhMHDx5MVf7X/Sb/7t69ewwcOBA/Pz+L8rx58xrf7e3tje8FChRg5cqV7Ny5k59++omlS5cSGhrKihUrKFSoUKr+mzRpwoYNGyhXrhxnzpx56InZR48epV27dtStW5fKlSsTFBTEggULHhnzX+XOnfuh4/vpp5/o3bs3b731Fv7+/vTu3Zthw4ZZ3POww2XMZrPFeP8qJSWFlJSUh9Y/WMr+YA/Ll1566bFvTObKlcv4O6eFra3tQ8sPHTpEhw4dqFevHlWqVKFr1658++23T2xrNpsf2Sf835yZNm0a7u7uFnXpuURdREREREREREQy3lPvMWlnZ8fo0aM5evQo8+bNA8Dd3Z1bt25x79493NzcjOTWuHHjSEhIMJJNfz04Ji4uzvhuZWVl8Qx3d3cuXLhg9OXm5sa3337L7t27HxrTtm3bWLp0Kf7+/gwdOpR169Zx8+ZN9u3b99D7AwMDiY6OZs2aNfj5+RlLyf8qPDycSpUqMXnyZDp06ICXlxenT59+rhPEly5dSsuWLQkJCaFVq1a4u7tz9uzZNPVZokQJbGxs+O2334yyK1eucObMGdzd3SlVqhSxsbFcv37dqN+/fz+5cuUy3mycP38+JpPpmeNPq9WrV1O1alUmTZpE+/bt8fT0TPNvlz9/fpydnTly5IhRFhMTQ82aNbG1tcXZ2Zn4+HhjXri6ujJp0iSOHz+ekUMSEREREREREZF09kyH33h5edGyZUu++uorLl68SKlSpfD392fAgAH89ttvHD16lIEDB3L58mUKFixI6dKlyZ07NzNmzODs2bPMmzePw4cPG/09eBPy8OHD3L17l65du7Jw4UJWrVrFmTNnmDFjBitWrKBkyZIPjSclJYUJEyawbt064uLiiIiIIDEx0diD8u/KlClD0aJFmT9/PoGBgQ+9x9nZmePHj/Pbb78RGxvLZ599xn//+19jufKzcHZ2Zv/+/Rw7dozjx48bv9GDk7Mfx8HBgVatWhESEsLu3bs5evQon376Ka6urtSsWRM/Pz+KFSvGgAEDOHbsGDt37mTs2LE0a9bMeJswPj4+Uw6JcXZ25tixYxw4cIBTp04xZswYDh06lKZxAnTq1Ilp06axc+dOTpw4wahRo/D09MTR0ZGgoCCmTp3K5s2bOX36NMOHD2fXrl2PnBsiIiIiIiIiIvJieqbEJEDfvn2xtbVl/PjxAEyYMAE3Nze6detGx44dKViwIDNnzgTuJ9VGjRrF5s2badq0KQcPHqRz585GX6+99hp+fn60b9+e7du3ExgYSL9+/ZgxYwZNmjRh48aNfPnll6n2bHwgICCAPn36MGHCBBo1asS8efOYNGnSY5NVTZo0ISUlhXr16j20vlOnTlSqVImuXbvStm1bzp07R+/evS3e5Htan3zyCfnz56dNmzZ069aNvHnz0rZt2zT3GRwcTPXq1fnoo49o164defPmZf78+djZ2WFjY8PMmTNJSUmhdevWxgFAISEhwP1l0H5+fqxbt+6Z40+rLl264O3tTZcuXWjfvj0XL17kww8/tEhGP84HH3xAw4YN6du3L23atCF//vzG4Uvdu3enZcuWDB8+nObNm3Py5Enmzp2bKW+CioiIiIiIiIhI+rEyP8/aZJFsqvvaPcTfyvi3R0VE/olWveNHfPyNrA5DMpjJ5Ki/s2QozTHJDJpnktE0xyQzZId5Zm1tRYECDk/fLgNiEREREREREREREXmsNJ3KLfJP83Xj17M6BBGRbOtOUnJWhyAiIiIiIv8ASkxKjnT5cgIpKdrFQDJGdnjNXrI/zTMREREREcnutJRbREREREREREREMp0SkyIiIiIiIiIiIpLplJgUERERERERERGRTKc9JiVHepYj7EWehsnkmNUhSA6Q2fPsTtI9bly9lanPFBERERGRfy4lJiVH6rX2d+JvJWV1GCIi2cqyd8qh43ZERERERCS9aCm3iIiIiIiIiIiIZDolJsVCcHAwHh4ej/ysXLmS6dOn065du3R7ZkBAAGFhYanKo6Oj8fDwMK49PDyIjo5O1aZTp05MmTIl3eIREREREREREZGMp6XcYmHIkCH069cPgL1799KnTx+ioqKMekdHR+bMmZMlsUVFReHk5JQlzxYRERERERERkfSlxKRYcHR0xNHx/mEKD5KAJpMpK0MyvChxiIiIiIiIiIjI89NSbnkmycnJjB49msqVK1O9enXmzp1r1JnNZmbOnIm/vz+VK1fm3XffJTY29rmf+del3CIiIiIiIiIikr0pMSnP5MCBAwCsWrWKDz74gM8//5xjx44BsGjRIsLDw5kwYQLLli3Dzc2NoKAgbt++nZUhi4iIiIiIiIjIC0SJSXkmJpOJwYMH88orrxAUFMRLL71kJCbnzp1L//79qV69OqVKlWLYsGHY2Niwfv36R/YXEhKCj4+PxadHjx6ZNRwREREREREREclk2mNSnomrqyvW1v+X13Z0dOTu3bvcvHmTCxcu0L9/f4v6u3fvPnY5d+/evWnUqJFF2f79+xk4cGC6xy4iIiIiIiIiIllPiUl5Jn9NOv7VvXv3AJg8eTKvvvqqRd2DQ3UexsXFBTc3N4uyc+fOPWeUIiIiIiIiIiLyotJSbklXL730EgUKFCA+Ph43Nzfc3NwoVqwYkydPNpZ6i4iIiIiIiIiIKDEp6S4oKIhp06axadMmTp8+zciRI4mOjqZkyZJZHZqIiIiIiIiIiLwgtJRb0t27777L7du3GTlyJNevX6ds2bJ88803FCpUKKtDExERERERERGRF4SV2Ww2Z3UQIpmt19rfib+VlNVhiIhkK8veKUd8/I2sDkMyicnkqL+3ZCjNMckMmmeS0TTHJDNkh3lmbW1FgQIOT98uA2IREREREREREREReSwlJkVERERERERERCTTaY9JyZG+bFw6q0MQEcl27iTdy+oQRERERETkH0SJScmRLl9OICVF26tKxsgO+39I9qd5JiIiIiIi2Z2WcouIiIiIiIiIiEimU2JSREREREREREREMp0SkyIiIiIiIiIiIpLptMek5EgFCjhkdQjyD2cyOWZ1CJIDZNY8S0y6x7WrtzLlWSIiIiIiknMoMSk50tT1F7l6S6fLioikxYi3imZ1CCIiIiIi8g+kpdwiIiIiIiIiIiKS6fTGZDoKCAjg3LlzqcpLly5NZGQkwcHBJCcnM3HixCyI7r5du3bRuXNnDh06RK5cj//zr1y5kqlTp7J9+/ZUdY8a6wObN29m0KBBVKpUib59+z533CIiIiIiIiIi8s+ixGQ6Cw4OpmnTphZlDxKAQ4YMyYqQLPj4+BAVFfXEpOSTLF++nHv37i+FnjNnDgcOHGD69OlGvYuLC9OnT8fW1va5niMiIiIiIiIiIv9MSkymMwcHB0wm00PrHB2z/jAMOzu7R8b3NFxcXIzvefLkwdbWNlW/zs7Oz/0cERERERERERH5Z9Iek5koODiY/v37AzB9+nT69u1LSEgIlStXpm7dusyePdu4NyEhgSFDhlC9enUqVKhAw4YNWb9+vVHv4eHB6tWradasGT4+PnTq1IkzZ84Y9YcPH6Zjx454e3tTr149li9fDtxfyu3h4UFycjIA+/fvp3379nh7e1OxYkXeffddLl68mC7j7dSpE1OmTDHG/tlnn9G3b1+8vb1p0qQJR48eZcqUKVSpUoXatWuzYcMGo+2FCxf48MMPqVixInXq1GHixIkkJiYCkJSUxPDhw6levTre3t507dqVkydPpkvMIiIiIiIiIiKSOZSYzEIbN27ExsaGsLAw2rRpw+TJkzlx4gQA48aNIyYmhm+//ZbIyEhef/11hg0bZiTnAGbMmMHgwYP57rvvuHTpEpMnTwbgypUrBAUFUbJkSVatWkXfvn0ZMWIEe/futXh+QkICH3zwATVq1CAyMpJvvvmGuLg4vvrqqwwZ76JFi6hcuTLh4eE4OjrSqVMn/vzzT5YuXUrNmjUZNmwYZrMZs9lMr169cHJyYsWKFUycOJFt27YZ4wsNDWXHjh3Mnj2bH374gXz58jFo0KAMiVlERERERERERDKGlnKns5CQEMaOHWtRtmnTJgoUKJDqXkdHR4KDg7GxsaFHjx7MmzePgwcP8uqrr1K5cmU6d+6Mh4cHAN26dSMsLIyLFy9SvHhxALp06UL16tUBaNeuHQsWLABg7dq15MuXj+HDh2NjY0PJkiW5evUqKSkpFs+/ffs2H3zwAd26dcPKyj2rXocAACAASURBVIrixYvzxhtvsH///nT/XQDKlClDx44dAWjSpAnjx49nyJAh2Nvb07FjR1asWMGff/7JsWPHiIuLY9myZdjY2ADw73//m27dutG/f3/i4uLInTs3xYoVw8XFhREjRhAbG5shMYuIiIiIiIiISMZQYjKd9e7dm0aNGlmUPWqvRVdXVyPxBpAvXz6SkpIAaNGiBZs2bSIsLIyTJ09y6NAhAIvk4iuvvGJ8d3BwMJZnnzhxgjJlylj0/SAhuGvXLqPMZDLx1ltvMX/+fI4cOcKJEyc4duwYXl5ezzT2J3mQUAXInTs3L7/8Mvb29gDGv4mJicTExHD9+nWqVKli3G82m0lKSuL8+fN07NiRTZs24e/vT6VKlahXrx7vvPNOhsQsIiIiIiIiIiIZQ4nJdObi4oKbm1ua7n3cidUDBgzg119/5c0336Rdu3aYTCbatGnz2PZms/mJ/f7VxYsXeeeddyhbtix+fn60bt2abdu2sW/fvjS1f1p/TZQCWFs/fCeB5ORk3NzcLPbcfKBw4cLY2dmxfv16tm/fzrZt25g1axbLli1j5cqV5M6dO0NiFxERERERERGR9KXE5AsoISGByMhIFi9ejI+PDwA//fQT8H/Jx8dxc3Nj06ZNpKSkGMm/QYMGUbhwYapVq2bct3HjRvLly8ecOXOMsoULF6bpGRnJ3d2dCxcu4OzsjJOTEwB79+7lu+++4/PPP2f16tXY2trSpEkTGjRowEcffUTt2rU5duwY3t7eWRq7iIiIiIiIiIikjQ6/eQHZ29uTJ08eNmzYQFxcHFFRUYSEhABYHH7zKM2bN+fmzZuMHTuWU6dOERkZSWRkJP7+/hb3OTs787///Y8dO3Zw9uxZvv76azZs2JCmZ2QkPz8/ihcvTv/+/Tl69Cj79+9n6NChWFtbY29vz40bNxgzZgxRUVHExcWxYsUK8ubNS4kSJbI0bhERERERERERSTslJl9Atra2fP7552zatInAwEDGjh1Ljx49KFSoEIcPH35ie0dHR77++msOHDhA8+bNmT59OmPHjqVSpUoW9zVu3JjmzZvTp08f3n77bX755RcGDRrEqVOnuHPnTkYN74lsbGyYOXMmNjY2tG3blh49elClShVGjx4NQIcOHXj77bcZNGgQjRs3ZvPmzcyePdt4u1JERERERERERF58VuasXrcrkgWmrr/I1Vv3sjoMEZFsYcRbRYmPv5HVYUgmM5kc9XeXDKU5JplB80wymuaYZIbsMM+sra0oUMDh6dtlQCwiIiIiIiIiIiIij6XEpIiIiIiIiIiIiGQ6ncotOVKfhoWyOgQRkWwjMUlbX4iIiIiISPpTYlJypMuXE0hJ0faqkjGyw/4fkv1pnomIiIiISHanpdwiIiIiIiIiIiKS6ZSYFBERERERERERkUynpdySIz3LEfYiT8NkcszqECQHSI95lpSUwtWrN9MhGhERERERkaejxKTkSKvXXubmrZSsDkNEJMt1eMeU1SGIiIiIiEgOpaXcIiIiIiIiIiIikumUmBQREREREREREZFMp8TkCyQgIICwsLBU5dHR0Xh4eFiUhYaG0rRpUzw9PalWrRr/+te/OHv27EP7bdSoEb6+viQmJj7x+R4eHo/8xMXFAWA2m1myZAktWrTAy8uLmjVr0qdPH44fP27RX3BwsEV7T09PAgMDWbBgAWazOdXzExMTqVy5Ms2aNbMoT0hIwN/fn379+qVqc+PGDWrWrMm4ceMeOzYREREREREREXmxKDGZDYWGhjJr1iz69OnD2rVr+frrr7l58yYdO3bk5k3LAwwOHTpEfHw81tbWbNu27bH9Ll++nKioKKKioujSpQs+Pj7GdVRUFEWKFAFg2LBhTJ06lfbt2/Pjjz8ye/Zs8ubNS5s2bdi1a5dFnw0bNjTaR0ZG0qVLF6ZOncqMGTNSPX/btm04OTkRExPDoUOHjHIHBwcGDRpEZGQke/bssWgzbdo07Ozs+Pjjj5/mJxQRERERERERkSymw2+yoRUrVhAUFET9+vUBKFasGFOmTKF69eps27aNJk2aGPdGRkZSqVIlXnrpJVatWsUbb7zxyH5dXFyM73ny5MHW1haTyfJQhE2bNrF69WqWL19OmTJlAChevDhjx47Fzs6OQYMGsW7dOuzs7ACws7Oz6MPNzQ1ra2tGjhxJ69atKVSokEWstWrV4ujRo6xatYry5csbdYGBgSxfvpxRo0axatUqbGxsOH78OIsXL2bGjBnky5fvWX5KERERERERERHJInpjMpvas2ePxdLsvHnzEh4eTu3atY0ys9nMmjVrqFq1KnXr1mX79u1cvnz5uZ4bFhZGQECAkZT8q969e3P+/HmioqIe20fz5s2xtbVl+/btRllCQgLbtm0zYo2MjCQpKcmi3bBhwzh58iRLly4FYMyYMdSvX5+6des+15hERERERERERCTzKTGZDXXu3JmtW7fi7+/Pp59+ysqVK7l06RIlSpTAwcHBuG/Pnj1cuHCBgIAAateujZWVFREREc/17P/+9794eXk9tO7ll1+mRIkS/Pbbb4/tw97enmLFinHixAmjbOPGjaSkpODv70+9evX4888/+emnnyzaubu78+677zJjxgwiIyM5dOgQQ4YMea7xiIiIiIiIiIhI1lBiMhtq0aIF3377LZ6enqxbt45BgwZRq1Ytxo4dS0pKinFfZGQkJUqUoFSpUjg6OuLr68vq1auf69nXrl3DycnpkfVOTk78+eefT+zHwcHBYj/MyMhIfH19cXR05NVXX6VkyZKsWrUqVbuePXuSJ08eBg4cSP/+/SlYsOCzDURERERERERERLKUEpMvkFy5clkkFh9ISUkhVy7L7UBr1qzJ3Llz2b17N7Nnz6ZBgwYsWLCAhQsXApCUlMT69euNfSgB3njjDY4cOcLRo0efOUYnJyfi4+MfWf+///0PZ2fnJ/aTkJBgvN155coVfvnlF+rVq2fUN2jQgJ9++okrV65YtMudOzdBQUHkz5+fNm3aPOMoREREREREREQkqykx+QJxdHQkISEhVfn169dxdHQE4I8//mDEiBHG24Z58uShTp06TJs2jYYNGxIdHQ3Ajh07uHr1Kt9++y3lypWjXLlyjBw5EuChbyKmlbe3NwcPHnxoXXx8PBcvXnzkUu8H7t69S2xsLKVLlwZg7dq1JCcnM2rUKCPWOXPmkJSURGRkZKr2uXPnxs7ODisrq2ceh4iIiIiIiIiIZC0lJl8gHh4e7N+/P1X5/v37KVeuHHD/lOtly5axbdu2VPc5OjoaJ2tHRERQokQJwsPDWb16tfGpU6cOERERJCcnP1OM7du3Z8uWLRw4cCBV3fTp0ylYsCC1atV6bB8RERFYWVkZh9ZERERQtWpVi1jDw8MpW7bscy89FxERERERERGRF1OuJ98imaVDhw60adOG6dOn07RpU5KTk4mKiiIsLIzp06cDUKBAAdq1a8e///1vrly5gr+/P3fu3CE6Opo1a9YQGhrK7du32bJlCz179uS1116zeEbnzp3p1q0bP//88zOdZu3v70+HDh3o3r07/fr1o1q1aty8eZMlS5YQERHBrFmzsLOzM+5PTEw0ln7funWLn3/+mcmTJ9OzZ09cXFw4d+4c//nPf5g0aVKqWNu3b8+wYcM4fvx4qjoREREREREREcnelJh8gZQvX565c+fy5ZdfMn/+fFJSUihdujRTpkzB39/fuG/w4MEUL16csLAwJk2ahJWVFV5eXnzzzTeUK1eONWvWcPfuXVq0aJHqGTVq1MDNzY1Vq1Y9U2ISYNiwYVSoUIHQ0FDGjh1Lnjx58PX1ZdmyZcby7AfWr1/P+vXrgfsH3ri7uzN06FDefvttAH788UecnJxo0KBBquc0bdqU8ePHs2rVKgYOHPhMsYqIiIiIiIiIyIvJymw2m7M6CJHMtnrtZW7eSn3QkIhITtPhHRPx8TeyOgx5AZlMjpobkqE0xyQzaJ5JRtMck8yQHeaZtbUVBQo4PH27DIhFRERERERERERE5LGUmBQREREREREREZFMpz0mJUdq0bhAVocgIvJCSErSthYiIiIiIpI1lJiUHOny5QRSUrS9qmSM7LD/h2R/mmciIiIiIpLdaSm3iIiIiIiIiIiIZDolJkVERERERERERCTTaSm35EjPcoS9yNMwmRyzOgTJAZ5nniUlpXD16s10jEZEREREROTpKDEpOVJU+GXu3NSBDyKSc9Vvb8rqEEREREREJIfTUm4RERERERERERHJdEpMioiIiIiIiIiISKZTYlLSLC4uDg8Pj0d+Vq5caXFdvnx5atWqxejRo0lISDD62bVrFx4eHnz//fepnhEcHEz//v3TFE9iYiJLlixJt/GJiIiIiIiIiEjm0R6TkmZFihQhKirKouz27dt06dKFV199FQCTycSqVauA+4nDEydOMGbMGI4fP878+fOxtv6/XPiUKVNo2LAhBQoUeKZ4fvzxR2bOnEnbtm2fcUQiIiIiIiIiIpJV9MakpJmNjQ0mk8niM336dG7dusXYsWMBsLa2NupcXV2pXbs2s2bNYt++fWzcuNGiP0dHR8aPH//M8ZjN5ucaj4iIiIiIiIiIZB0lJuWZrVmzhh9++IGQkBBMpkef7lqyZEmqVKmSKjE5ZMgQwsPD2bNnzyPb7t27l5YtW+Ll5UWTJk1YvXo1cH85+KBBg7h48SIeHh7ExcWlz6BERERERERERCRTKDEpz+TixYuMHDmSFi1a0LBhwyfe/+qrrxITE2NRVrt2berXr8/IkSNJSkpK1SY+Pp7u3bvTrFkzIiIi6NWrF6NHj2bLli34+PgwePBgTCYTUVFRFClSJN3GJiIiIiIiIiIiGU+JSXlqZrOZwYMHky9fPoYNG5amNg4ODty8eTNV+dChQzl37hzz589PVRcaGoqvry9dunTBzc2NwMBAgoKCWLBgAXZ2djg6OhpLx21sbJ53WCIiIiIiIiIikol0+I08tUWLFhEdHc13332Hg4NDmtrcvHnzofcWKVKEnj17MnPmTJo2bWpRd/LkSX7++Wd8fHyMsuTkZFxcXJ5vACIiIiIiIiIikuWUmJSnEhMTw8SJE+natSuvv/56mtsdO3aM0qVLP7Sua9euhIeHM2bMGIvkZXJyMk2aNOHDDz+0uP+vJ3uLiIiIiIiIiEj2pAyPpFlycjIDBgzAzc2NPn36pLldbGwse/fupVGjRg+tt7W1Zfjw4WzcuJHdu3cb5e7u7sTGxuLm5mZ8oqKiWL58OQBWVlbPNyAREREREREREckySkxKmn311VccO3aMQYMGce3aNeLj4y0+9+7dIyUlxbg+f/48mzZt4v3336d69erUrVv3kX1XrVqV5s2bc+7cOaOsffv2HDlyhEmTJhEbG8u6dev4/PPPKVSoEAB58+blxo0bnDp1iuTk5Awfv4iIiIiIiIiIpB8t5ZY02717N0lJSQQFBT20fty4ccTHx+Pn5weAvb09RYsWpVmzZrz33ntP7D84OJht27YZ166ursyePZuJEycyb948TCYTH330Ee3btwegWrVqlCxZkubNm7N48WIqVKjw3GMUEREREREREZHMYWU2m81ZHYRIZosKv8ydmylZHYaISJap395EfPyNrA5DXmAmk6PmiGQozTHJDJpnktE0xyQzZId5Zm1tRYECaTsg2aJdBsQiIiIiIiIiIiIi8lhayi05kt+bBbI6BBGRLJWUpLfGRUREREQkaykxKTnS5csJpKRoFwPJGNnhNXvJ/jTPREREREQku9NSbhEREREREREREcl0SkyKiIiIiIiIiIhIplNiUkRERERERERERDKd9piUHOlZjrAXeRomk2NWhyA5wJPmWXJiCn9eu5lJ0YiIiIiIiDwdJSYlRzq05BKJCTqRVkT+2XzeK5jVIYiIiIiIiDySlnKLiIiIiIiIiIhIpsuxiUkPDw88PDw4e/ZsqrrFixfj4eHBlClTjLKzZ8/Sp08ffH198fLyonnz5nz//fcW7QICAox+PTw8KF++PPXq1ePrr7/O8PGIiIiIiIiIiIhkJzl6KbetrS1btmyhS5cuFuWbNm3CysrKuL5z5w6dO3fGz8+PBQsWkC9fPvbs2cOoUaNISkqyaB8cHEzTpk0BSE5O5pdffmHIkCEULFiQFi1aZM7AREREREREREREXnA59o1JgCpVqrBlyxaLsoSEBPbv30+5cuWMsujoaK5du0ZISAhlypShePHivP322wQFBbF48WKL9g4ODphMJkwmE0WKFOGtt96ievXqbNiwIVPGJCIiIiIiIiIikh3k6MRkvXr12LdvHzdu3DDKfvrpJ6pUqUK+fPmMMisrK27fvs2vv/5q0b5r167MmTPnic/JlSsXdnZ2AKSkpDB37lzq16+Pl5cXHTt25OjRowD07NmTsWPHGu3GjBnD66+/jtlsBuDgwYP4+PiQmJhIYmIiY8aMoVq1avj6+vLJJ59w6dIlAOLi4vDw8ODLL7/k9ddfZ9CgQUyfPp127dpZxBUQEEBYWBgAx44do0OHDlSsWJGaNWvy2WefkZycDIDZbGbmzJn4+/tTuXJl3n33XWJjY41+PDw8mDp1KtWqVSMoKOihv8GPP/5IkyZN8Pb2pmXLluzfv9+o27p1K2+99RZeXl40btyYtWvXGnWdOnXi66+/pmvXrnh5edGqVSvOnDnD0KFD8fHxoWHDhuzdu/eJfwMREREREREREXmx5OjEZKlSpXB1dWX79u1G2ebNm6lfv77FfTVq1KBUqVJ06NCBNm3aMG3aNPbu3YuDgwPFixd/ZP9JSUls2LCBHTt2EBAQAMCXX37Jt99+y6BBg1i1ahXFihXjvffeIyEhAT8/P3bt2mW037NnDzdu3OD3338H7r+56evri52dHZMnT+Y///kPs2fPZuHChZjNZj744AMjiQmwd+9eVqxYQffu3Z/4W3z66aeULFmSiIgIpk6dSnh4OMuXLwdg0aJFhIeHM2HCBJYtW4abmxtBQUHcvn3b4nf7/vvvGTJkSKq+d+7cyYABA2jfvj0//PADvr6+fPDBByQkJLBz504++ugj3nzzTcLDw2nTpg39+/fnwIEDRvuvvvqKVq1asXLlSq5du8Y777xD4cKFWb58OW5ubhbJXBERERERERERyR5ydGIS7r81+GA5d1JSElFRUUYS8QF7e3sWL17M+++/z6VLl5g5cyYdOnSgcePGHDx40OLekJAQfHx88PHxwcvLi4EDB9KlSxeaN2+O2Wxm0aJF9O7dm3r16lGqVClGjRpFrly5CA8Px9/fn2PHjnH16lWuX7/O6dOnef311403NaOjo/H39+f27dssWrSIkSNH4u3tzWuvvcaECRM4ceIE+/btM2Lp3Lkzr7zyCu7u7k/8Hc6dO0f+/PkpWrQor7/+OnPmzMHPzw+AuXPn0r9/f6pXr06pUqUYNmwYNjY2rF+/3mjfpk0bSpYsSenSpVP1vXjxYho3bkyHDh1wc3OjX79+tGzZkuvXrxMaGkr9+vUJCgrC3d2doKAg3njjDebOnWu0r1WrFoGBgbz66qsEBATg4OBA7969KVWqFK1ateLkyZNPHJ+IiIiIiIiIiLxYcvThN3B/OfeHH35oHFTz6quv8vLLL6e6z9HRkX79+tGvXz9OnjzJtm3bmDdvHj169GDz5s3Y29sD0Lt3bxo1agTcT2iaTCZsbGwAuHz5MlevXsXb29vo19bWlgoVKhATE0OHDh0oXrw4e/fuxdraGk9PT6pUqcK+ffto0aIFv/76K6NGjeLs2bMkJSXRoUMHixjv3r3LqVOnKFy4MACurq5p/h3+9a9/MXr0aJYuXUqtWrVo0qQJFSpU4ObNm1y4cIH+/ftjbf1/eey7d+9aLOd+3LNiYmJo1aqVcW1tbc2AAQOMutatW1vc7+Pjw7Jly4zrv76Vam9vT9GiRS2uExMT0zxOERERERERERF5MeT4xGSlSpWwsbFh3759bN68mQYNGqS6Z9myZeTJk4dmzZoBULJkSUqWLImfnx/NmjXj+PHjeHp6AuDi4oKb2/9r787jc7rW/o9/EzGTEE1MhxCVEIkkhBqSlCiVGGqog6JH6TFVqaGG1qweHqeGymmLlnJQWgcJmnIUrYa2CKIIkpg5JWaRRCL3/v3hZz+9myBRSUQ+79frftW99tp7X3v36pJc3Wttl0zPdb94+Ufp6elKT0+XJHM6t52dnfz8/OTn56fw8HDt3btXFStWVJUqVRQTEyNJWrZsmUqXLm11LEdHR924cSPD+X7/lvH77q8hKUk9evRQ8+bNtXXrVn3//fcaNGiQBg4caL5xfPbs2Xr++eet9v/9uR90bdK94uuDZLafxWIx74cks7B73+8LpAAAAAAAAMifCnyFx9bWVs2aNdO2bdu0ffv2DOtLStLx48e1YMECq0KeJNnb20u6VwzMitKlS8vJyUnR0dFmW1pamg4fPmxOt75fmNy7d6/8/Pzk4+Oj3377TWvXrlVgYKCke08QFipUSNeuXZOLi4tcXFzk6Oio6dOn6/z585meu3DhwkpMTDS/JyUl6erVq5LuPf34wQcfyMbGRr169dKiRYs0ePBgRUREyN7eXuXKlVNCQoJ5rr/85S+aPXu2jh07lqXrdnFx0ZEjR8zvhmEoJCREO3bsUI0aNazuhyTt378/S9PPAQAAAAAAkH8V+MKkdG869+rVq1WmTJlMX2bz+uuv6+LFixo4cKD27Nmjc+fOKTIyUsOGDVNwcHC2pkz36dNH//znP7V161bFx8drwoQJunPnjtq2bStJatSokU6ePKnjx4/Lx8dHJUqUkIeHhyIiIhQQECBJKlWqlLp06aKpU6fqp59+Unx8vEaPHq3jx4+rWrVqmZ7Xy8tLx48fV3h4uE6dOqUJEyaYTx4WLVrUnCYeHx+vY8eOaceOHapTp44kqXfv3vroo4/03Xff6fTp05o8ebJ27dolV1fXB15nQkKCUlJSzPsXERGh1atX6/Tp0/rwww9148YN+fr6qnfv3tqyZYuWLFmiU6dOacmSJdqyZUuGaeoAAAAAAAB4thT4qdzSvacULRZLpk9LSlLVqlW1atUqffTRRxo6dKhu3rwpZ2dntWvXToMGDcrWuXr37q3ExERNnDhRt27dko+Pj5YtW2aua1myZEn5+PgoOTlZJUqUkCT5+fnp2LFjatiwoXmcsWPHaubMmRo2bJju3LmjevXqadGiRSpWrFim523cuLH69u2rDz74QLa2tvrb3/6mevXqmdvnzJmjKVOmmOs9Nm/eXOPHj5ck9e3bV8nJyZo8ebJu3ryp2rVra9GiRSpfvvwDr9Pf31/Tp09Xp06dVL9+fU2dOlUff/yxpk6dKg8PDy1YsEClS5eWl5eXPvzwQ82bN08ffvihqlevrrlz56pp06bZuq8AAAAAAADIX2wMwzDyOgggtx1edVmpiZa8DgMAcpTvm85KSLiV12Egn3JyKk3+IEeRY8gN5BlyGjmG3JAf8szW1kblypXK/n45EAsAAAAAAAAAPBSFSQAAAAAAAAC5jjUmUSDV6fZcXocAADnubipLVgAAAAB4elGYRIF05UqiLBaWV0XOyA/rfyD/I88AAAAA5HdM5QYAAAAAAACQ6yhMAgAAAAAAAMh1TOVGgfQ4r7AHssPJqXReh4ACILM8S0+16OqN23kQDQAAAABkD4VJFEgXPktQ+s30vA4DAJ64KiMq5HUIAAAAAJAlTOUGAAAAAAAAkOsoTAIAAAAAAADIdRQmn3FBQUFyd3eXu7u7atWqJV9fX3Xr1k0//vhjlo/xyy+/yN3dXXfv3tW5c+fk7u6u06dPZzuW+3GcPXs2w7aVK1fK3d1dc+bMydKxevXqZfYdM2aMRo4cme14AAAAAAAAkHcoTBYAY8aMUWRkpH744Qd99dVXqlevnvr3769du3ZlaX9fX19FRkbKzu7PL0lauHBhbdu2LUP7d999JxsbmywfJzQ0VP369fvT8QAAAAAAACBvUJgsAEqVKiUnJyeVL19ebm5uGjVqlNq0aaPp06dnaf8iRYrIycnpicTi5+eXoTCZmJio/fv3y8PDI8vHKVOmjEqWLPlEYgIAAAAAAEDuozB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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grab the top 25 news sources\n",
    "freq_sources = main_data.Source.value_counts()[0:25]\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "# Source names go on the y-axis, so that the plot is created horizontally\n",
    "sns.barplot(x = freq_sources.values, y = freq_sources.index)\n",
    "\n",
    "# Titles and labels\n",
    "plt.title('Top 25 News Sources by Article Count', fontsize=20)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the data into their respective categories\n",
    "obama_data = main_data[main_data['Topic'] == 'obama']\n",
    "economy_data = main_data[main_data['Topic'] == 'economy']\n",
    "palestine_data = main_data[main_data['Topic'] == 'palestine']\n",
    "microsoft_data = main_data[main_data['Topic'] == 'microsoft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize a large 2x2 grid of subplots, share x and y axes between plots\n",
    "fig, ax = plt.subplots(2, 2, figsize = (14, 10), sharex = True, sharey = True)\n",
    "\n",
    "# Collect data, subplots, desired colors, and titles into lists for easy plotting\n",
    "list_all = [obama_data, economy_data, palestine_data, microsoft_data]\n",
    "subplots = [ax[0][0], ax[0][1], ax[1][0], ax[1][1]]\n",
    "colors = list(sns.husl_palette(10, h=.5)[1:5]) \n",
    "titles = ['Obama', 'Economy', 'Palestine', 'Microsoft']\n",
    "\n",
    "# Create all 4 subplots with one for loop using zip\n",
    "for data, subplot, color, title in zip(list_all, subplots, colors, titles):\n",
    "    sns.distplot(data['SentimentTitle'], kde=False, ax=subplot, color=color, \n",
    "                 hist_kws = dict(edgecolor='k', linewidth=1))\n",
    "    subplot.set_title(title, fontsize=14)\n",
    "    subplot.set_xlabel('')    \n",
    "\n",
    "# Add final tweaks: overall title, limits on x-axis, reduce number of y ticks\n",
    "fig.suptitle('Title Sentiment by Topic', fontsize=20)\n",
    "plt.xlim(-0.5, 0.5)\n",
    "plt.yticks(np.arange(0, 8001, 2000))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot gives us a lot of valuable information about title sentiment in our data.\n",
    "\n",
    "1. We can see at a glance that **Obama** and **Economy** are the most prevalent topics in this dataset. \n",
    "2. We can see that the title sentiments for all four topics vaguely resemble **normal distributions**, although they are much flatter than is typical. It's unclear if this emerged naturally, or if the researchers purposefully shaped the data such that this would be the case.\n",
    "3. We can also see that all four topic sentiments are highly **unimodal**, centered around 0. A large proportion of the headlines in our dataset are thus considered \"neutral\" in terms of their sentiment.\n",
    "\n",
    "Let's create the same plot for headlines, to see whether the trends will differ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatic pdb calling has been turned OFF\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlenechambliss/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize a large 2x2 grid of subplots, share x and y axes between plots\n",
    "fig, ax1 = plt.subplots(2, 2, figsize = (14, 10), sharex = True, sharey = True)\n",
    "\n",
    "# Define the new subplot list to avoid conflicts\n",
    "subplots = [ax1[0][0], ax1[0][1], ax1[1][0], ax1[1][1]]\n",
    "\n",
    "# The other lists already exist, so we can call them again and just change SentimentTitle to SentimentHeadline\n",
    "for data, subplot, color, title in zip(list_all, subplots, colors, titles):\n",
    "    sns.distplot(data['SentimentHeadline'], kde=False, ax=subplot, color=color, \n",
    "                 hist_kws = dict(edgecolor='k', linewidth=1))\n",
    "    subplot.set_title(title, fontsize=14)\n",
    "    subplot.set_xlabel('')    \n",
    "\n",
    "# Add final tweaks: overall title, limits on x-axis, reduce number of y ticks\n",
    "fig.suptitle('Headline Sentiment by Topic', fontsize=20)\n",
    "plt.xlim(-0.75, 0.75)\n",
    "#plt.yticks(np.arange(0, 4000, 1000))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very interesting! Compared to title sentiment, headlines are even more normally distributed. Instead of a median and mode at 0 with a flattened distribution, we see that most values tend to fall near 0 but not at precisely 0. I'd be interested to know if this might be a measurement artifact, or if it was a direct result of the data (the words used in titles vs. headlines). \n",
    "\n",
    "Let's take a look at some titles and headlines that are considered \"outliers\" for these distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_most_positive(data, column_name, most_negative=False): \n",
    "    '''\n",
    "    Returns the most positive title or headline by sentiment rating. Can be used to \n",
    "    get the most negative title/headline by specifying most_negative=True.\n",
    "    '''\n",
    "    # most_positive is False (an empty string) by default, in case an incorrect column is passed\n",
    "    most_positive = ''\n",
    "    \n",
    "    # Check for correct column, then locate the most positive title/headline\n",
    "    if column_name in ('SentimentTitle', 'SentimentHeadline'):\n",
    "    \n",
    "        # Default: most_negative is False, so find the most positive value with np.max\n",
    "        if most_negative == False:\n",
    "            data_most_positive = data[data[column_name] == np.max(data[column_name])]\n",
    "        else:\n",
    "            data_most_positive = data[data[column_name] == np.min(data[column_name])]\n",
    "\n",
    "        # Check whether we should return the title or the headline\n",
    "        if column_name == 'SentimentTitle':\n",
    "            most_positive = data_most_positive[:1]['Title'].values\n",
    "        else:\n",
    "            most_positive = data_most_positive[:1]['Headline'].values\n",
    "    \n",
    "    # Skip down to here if the column name is incorrect\n",
    "    else: \n",
    "        print('This function wasn\\'t written for that. Try passing \\'SentimentTitle\\' or \\'SentimentHeadline\\'.')\n",
    "    \n",
    "    # If most_positive is defined, return it\n",
    "    if most_positive:    \n",
    "        return most_positive[0]\n",
    "    else:\n",
    "        return ''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will freely admit that the above function has some wonky variable naming - I'll likely come back and rework this (and possibly refactor, if there is a more robust and efficient way of tackling the problem). The extra bit about returning an empty string at the end is to prevent the function from returning None by default.\n",
    "\n",
    "Let's test the function out a bit to make sure it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Microsoft Band 2: The Best Fitness Watch for People Who Really Like to Sweat\n",
      "The bicyclist, a 54-year-old Palestine resident, appeared to have head injury and was transported for treatment. \n",
      "This function wasn't written for that. Try passing 'SentimentTitle' or 'SentimentHeadline'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(get_most_positive(microsoft_data, 'SentimentTitle'))\n",
    "\n",
    "print(get_most_positive(palestine_data, 'SentimentHeadline', most_negative=True))\n",
    "\n",
    "print(get_most_positive(economy_data, 'Source'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most positive Obama title: Obama administration upbeat about health law sign-ups\n",
      "Most negative Obama title: Obama's stubborn, willful complacency on terror\n",
      "\n",
      "Most positive Economy title: We just got some really great news about the economy\n",
      "Most negative Economy title: Improving economy not helping all\n",
      "\n",
      "Most positive Palestine title: EP mural taking shape\n",
      "Most negative Palestine title: The Unbearable Lightness of Murder: The Tragedy of Palestine's ...\n",
      "\n",
      "Most positive Microsoft title: Microsoft Band 2: The Best Fitness Watch for People Who Really Like to Sweat\n",
      "Most negative Microsoft title: Satya Nadella just fixed a massive problem at Microsoft\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Recall that `titles` contains the four topics, `list_all` contains the four topic datasets\n",
    "\n",
    "# Print pairs of most positive and negative titles by sentiment\n",
    "for topic, data in zip(titles, list_all):\n",
    "    print('Most positive {} title: {}'.format(topic, get_most_positive(data, 'SentimentTitle')))\n",
    "    print('Most negative {} title: {}'.format(topic, get_most_positive(data, 'SentimentTitle', most_negative=True)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most positive Obama headline:  seems to be quite unconcerned about how the Secret Service wanted a word with him about what he has suggested about President Obama.\n",
      "Most negative Obama headline: &quot;Climate change is a massive problem,&quot; Obama said. &quot;It is a generational problem. It's a problem that by definition is just about the hardest thing\n",
      "\n",
      "Most positive Economy headline: It all depends on what the Fed thinks about the real economy, and whether to normal sooner means getting the real economy back to healthy,\n",
      "Most negative Economy headline: There's a sense this economy is just not coming back, at least not like it was before. Who knows whether that pessimism is correct, but in the\n",
      "\n",
      "Most positive Palestine headline: The Latest on Texas flooding (all times local): \n",
      "Most negative Palestine headline: The bicyclist, a 54-year-old Palestine resident, appeared to have head injury and was transported for treatment. \n",
      "\n",
      "Most positive Microsoft headline: Gregory &quot;Trey&quot; Thompson, a senior at Model High School, became the first student at Model High to achieve the status of \"\"\"Master\"\"\" of Microsoft\n",
      "Most negative Microsoft headline: While Fable Legends had a troubled development phase and a similar outcome was not quite inconceivable, Microsoft's decision to shut down\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Do the same for headlines\n",
    "for topic, data in zip(titles, list_all):\n",
    "    print('Most positive {} headline: {}'.format(topic, get_most_positive(data, 'SentimentHeadline')))\n",
    "    print('Most negative {} headline: {}'.format(topic, get_most_positive(data, 'SentimentHeadline', most_negative=True)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To be continued!\n",
    "\n",
    "# Next: popularity analysis"
   ]
  }
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