Social_Analytics_Homework.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import tweepy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.random import rand\n",
    "from itertools import cycle\n",
    "\n",
    "# Import and Initialize Sentiment Analyzer\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Twitter API Keys\n",
    "from config import (consumer_key, \n",
    "                    consumer_secret, \n",
    "                    access_token, \n",
    "                    access_token_secret)\n",
    "\n",
    "# Setup Tweepy API Authentication\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth, parser=tweepy.parsers.JSONParser())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @bbcworld\n",
      "Compound: -0.140\n",
      "Positive: 0.037\n",
      "Neutral: 0.855\n",
      "Negative: 0.108\n"
     ]
    },
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       "      <td>-0.3612</td>\n",
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       "      <td>0.095</td>\n",
       "      <td>0</td>\n",
       "      <td>BBC</td>\n",
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       "      <td>0.000</td>\n",
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       "      <td>BBC</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.3400</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>4</td>\n",
       "      <td>BBC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Compound  Negative  Neutral  Positive  Tweets Ago name\n",
       "0   -0.3612     0.194    0.711     0.095           0  BBC\n",
       "1    0.0000     0.000    1.000     0.000           1  BBC\n",
       "2   -0.3400     0.103    0.897     0.000           2  BBC\n",
       "3    0.0000     0.000    1.000     0.000           3  BBC\n",
       "4    0.0000     0.000    1.000     0.000           4  BBC"
      ]
     },
     "execution_count": 78,
     "metadata": {},
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   "source": [
    "# Target User Account\n",
    "target_user1 = \"@bbcworld\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list = []\n",
    "positive_list = []\n",
    "negative_list = []\n",
    "neutral_list = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweets = api.user_timeline(target_user1)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results[\"compound\"]\n",
    "        pos = results[\"pos\"]\n",
    "        neu = results[\"neu\"]\n",
    "        neg = results[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_list.append(compound)\n",
    "        positive_list.append(pos)\n",
    "        negative_list.append(neg)\n",
    "        neutral_list.append(neu)\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_user1}\")\n",
    "print(f\"Compound: {np.mean(compound_list):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_list):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_list):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_list):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "bbc_news = pd.DataFrame(\n",
    "    {'Compound': compound_list,\n",
    "     'Positive': positive_list,\n",
    "     'Neutral': neutral_list,\n",
    "     'Negative': negative_list\n",
    "    })\n",
    "bbc_news[\"Tweets Ago\"] =  range(len(bbc_news))  \n",
    "bbc_news[\"name\"] = \"BBC\"\n",
    "bbc_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @cbsnews\n",
      "Compound: -0.140\n",
      "Positive: 0.037\n",
      "Neutral: 0.855\n",
      "Negative: 0.108\n"
     ]
    },
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       "      <th>2</th>\n",
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       "      <td>0.000</td>\n",
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       "      <td>CBS</td>\n",
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      "text/plain": [
       "   Compound  Negative  Neutral  Positive  Tweets Ago name\n",
       "0   -0.3612     0.194    0.711     0.095           0  CBS\n",
       "1    0.0000     0.000    1.000     0.000           1  CBS\n",
       "2   -0.3400     0.103    0.897     0.000           2  CBS\n",
       "3    0.0000     0.000    1.000     0.000           3  CBS\n",
       "4    0.0000     0.000    1.000     0.000           4  CBS"
      ]
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     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Target User Account\n",
    "target_user2 = \"@cbsnews\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list2 = []\n",
    "positive_list2 = []\n",
    "negative_list2 = []\n",
    "neutral_list2 = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweets2 = api.user_timeline(target_user2)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results2 = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results2[\"compound\"]\n",
    "        pos = results2[\"pos\"]\n",
    "        neu = results2[\"neu\"]\n",
    "        neg = results2[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_list2.append(compound)\n",
    "        positive_list2.append(pos)\n",
    "        negative_list2.append(neg)\n",
    "        neutral_list2.append(neu)\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_user2}\")\n",
    "print(f\"Compound: {np.mean(compound_list2):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_list2):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_list2):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_list2):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "cbs_news = pd.DataFrame(\n",
    "    {'Compound': compound_list2,\n",
    "     'Positive': positive_list2,\n",
    "     'Neutral': neutral_list2,\n",
    "     'Negative': negative_list2\n",
    "    })\n",
    "cbs_news[\"Tweets Ago\"] =  range(len(cbs_news))  \n",
    "cbs_news[\"name\"] = \"CBS\"\n",
    "cbs_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @cnn\n",
      "Compound: -0.140\n",
      "Positive: 0.048\n",
      "Neutral: 0.865\n",
      "Negative: 0.087\n"
     ]
    },
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       "      <th>0</th>\n",
       "      <td>0.6249</td>\n",
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       "      <td>0.779</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0</td>\n",
       "      <td>CNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.3182</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>CNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1531</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.086</td>\n",
       "      <td>2</td>\n",
       "      <td>CNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.5106</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>CNN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.2500</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4</td>\n",
       "      <td>CNN</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Compound  Negative  Neutral  Positive  Tweets Ago name\n",
       "0    0.6249     0.000    0.779     0.221           0  CNN\n",
       "1   -0.3182     0.099    0.901     0.000           1  CNN\n",
       "2    0.1531     0.000    0.914     0.086           2  CNN\n",
       "3   -0.5106     0.191    0.809     0.000           3  CNN\n",
       "4   -0.2500     0.095    0.905     0.000           4  CNN"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Target User Account\n",
    "target_user3 = \"@cnn\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list3 = []\n",
    "positive_list3 = []\n",
    "negative_list3 = []\n",
    "neutral_list3 = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweets3 = api.user_timeline(target_user3)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets3:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results3 = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results3[\"compound\"]\n",
    "        pos = results3[\"pos\"]\n",
    "        neu = results3[\"neu\"]\n",
    "        neg = results3[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_list3.append(compound)\n",
    "        positive_list3.append(pos)\n",
    "        negative_list3.append(neg)\n",
    "        neutral_list3.append(neu)\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_user3}\")\n",
    "print(f\"Compound: {np.mean(compound_list3):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_list3):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_list3):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_list3):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "cnn_news = pd.DataFrame(\n",
    "    {'Compound': compound_list3,\n",
    "     'Positive': positive_list3,\n",
    "     'Neutral': neutral_list3,\n",
    "     'Negative': negative_list3\n",
    "    })\n",
    "cnn_news[\"Tweets Ago\"] =  range(len(cnn_news))  \n",
    "cnn_news[\"name\"] = \"CNN\"\n",
    "cnn_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @foxnews\n",
      "Compound: -0.222\n",
      "Positive: 0.033\n",
      "Neutral: 0.833\n",
      "Negative: 0.135\n"
     ]
    },
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.7964</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>Fox News</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>Fox News</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2263</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.128</td>\n",
       "      <td>3</td>\n",
       "      <td>Fox News</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4</td>\n",
       "      <td>Fox News</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Compound  Negative  Neutral  Positive  Tweets Ago      name\n",
       "0    0.0000     0.000    1.000     0.000           0  Fox News\n",
       "1   -0.7964     0.448    0.552     0.000           1  Fox News\n",
       "2    0.0000     0.000    1.000     0.000           2  Fox News\n",
       "3    0.2263     0.000    0.872     0.128           3  Fox News\n",
       "4    0.0000     0.000    1.000     0.000           4  Fox News"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target User Account\n",
    "target_user4 = \"@foxnews\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list4 = []\n",
    "positive_list4 = []\n",
    "negative_list4 = []\n",
    "neutral_list4 = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweets4 = api.user_timeline(target_user4)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets4:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results4 = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results4[\"compound\"]\n",
    "        pos = results4[\"pos\"]\n",
    "        neu = results4[\"neu\"]\n",
    "        neg = results4[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_list4.append(compound)\n",
    "        positive_list4.append(pos)\n",
    "        negative_list4.append(neg)\n",
    "        neutral_list4.append(neu)\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_user4}\")\n",
    "print(f\"Compound: {np.mean(compound_list4):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_list4):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_list4):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_list4):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "fox_news = pd.DataFrame(\n",
    "    {'Compound': compound_list4,\n",
    "     'Positive': positive_list4,\n",
    "     'Neutral': neutral_list4,\n",
    "     'Negative': negative_list4\n",
    "    })\n",
    "fox_news[\"Tweets Ago\"] =  range(len(fox_news))  \n",
    "fox_news[\"name\"] = \"Fox News\"\n",
    "fox_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @nytimes\n",
      "Compound: -0.291\n",
      "Positive: 0.030\n",
      "Neutral: 0.843\n",
      "Negative: 0.127\n"
     ]
    },
    {
     "data": {
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.8020</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.690</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
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       "      <td>3</td>\n",
       "      <td>New York Times</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.7579</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>New York Times</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound  Negative  Neutral  Positive  Tweets Ago            name\n",
       "0   -0.2500     0.124    0.796      0.08           0  New York Times\n",
       "1    0.0000     0.000    1.000      0.00           1  New York Times\n",
       "2   -0.8020     0.310    0.690      0.00           2  New York Times\n",
       "3    0.0000     0.000    1.000      0.00           3  New York Times\n",
       "4   -0.7579     0.277    0.723      0.00           4  New York Times"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target User Account\n",
    "target_user5 = \"@nytimes\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list5 = []\n",
    "positive_list5 = []\n",
    "negative_list5 = []\n",
    "neutral_list5 = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweets5 = api.user_timeline(target_user5)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets5:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results5 = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results5[\"compound\"]\n",
    "        pos = results5[\"pos\"]\n",
    "        neu = results5[\"neu\"]\n",
    "        neg = results5[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_list5.append(compound)\n",
    "        positive_list5.append(pos)\n",
    "        negative_list5.append(neg)\n",
    "        neutral_list5.append(neu)\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_user5}\")\n",
    "print(f\"Compound: {np.mean(compound_list5):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_list5):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_list5):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_list5):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "nytimes_news = pd.DataFrame(\n",
    "    {'Compound': compound_list5,\n",
    "     'Positive': positive_list5,\n",
    "     'Neutral': neutral_list5,\n",
    "     'Negative': negative_list5\n",
    "    })\n",
    "nytimes_news[\"Tweets Ago\"] =  range(len(nytimes_news))  \n",
    "nytimes_news[\"name\"] = \"New York Times\"\n",
    "nytimes_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @theeconomist\n",
      "Compound: -0.037\n",
      "Positive: 0.084\n",
      "Neutral: 0.812\n",
      "Negative: 0.104\n"
     ]
    },
    {
     "data": {
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       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>The Economist</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.1779</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.804</td>\n",
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       "      <td>1</td>\n",
       "      <td>The Economist</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.8834</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>-0.3612</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.762</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0.4019</td>\n",
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       "      <td>0.863</td>\n",
       "      <td>0.137</td>\n",
       "      <td>4</td>\n",
       "      <td>The Economist</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound  Negative  Neutral  Positive  Tweets Ago           name\n",
       "0   -0.2023     0.231    0.769     0.000           0  The Economist\n",
       "1   -0.1779     0.116    0.804     0.080           1  The Economist\n",
       "2    0.8834     0.000    0.405     0.595           2  The Economist\n",
       "3   -0.3612     0.238    0.762     0.000           3  The Economist\n",
       "4    0.4019     0.000    0.863     0.137           4  The Economist"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target User Account\n",
    "target_user6 = \"@theeconomist\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list6 = []\n",
    "positive_list6 = []\n",
    "negative_list6 = []\n",
    "neutral_list6 = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweets6 = api.user_timeline(target_user6)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets6:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results6 = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results6[\"compound\"]\n",
    "        pos = results6[\"pos\"]\n",
    "        neu = results6[\"neu\"]\n",
    "        neg = results6[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_list6.append(compound)\n",
    "        positive_list6.append(pos)\n",
    "        negative_list6.append(neg)\n",
    "        neutral_list6.append(neu)\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_user6}\")\n",
    "print(f\"Compound: {np.mean(compound_list6):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_list6):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_list6):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_list6):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "economist_news = pd.DataFrame(\n",
    "    {'Compound': compound_list6,\n",
    "     'Positive': positive_list6,\n",
    "     'Neutral': neutral_list6,\n",
    "     'Negative': negative_list6\n",
    "    })\n",
    "economist_news[\"Tweets Ago\"] =  range(len(economist_news))  \n",
    "economist_news[\"name\"] = \"The Economist\"\n",
    "economist_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @msnbc\n",
      "Compound: -0.156\n",
      "Positive: 0.037\n",
      "Neutral: 0.886\n",
      "Negative: 0.076\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2</td>\n",
       "      <td>MSNBC</td>\n",
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       "      <th>3</th>\n",
       "      <td>-0.4767</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>MSNBC</td>\n",
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       "      <th>4</th>\n",
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       "      <td>4</td>\n",
       "      <td>MSNBC</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound  Negative  Neutral  Positive  Tweets Ago   name\n",
       "0   -0.3412     0.118    0.882     0.000           0  MSNBC\n",
       "1   -0.7096     0.237    0.763     0.000           1  MSNBC\n",
       "2   -0.6369     0.162    0.791     0.047           2  MSNBC\n",
       "3   -0.4767     0.162    0.838     0.000           3  MSNBC\n",
       "4    0.0000     0.000    1.000     0.000           4  MSNBC"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target User Account\n",
    "target_user7 = \"@msnbc\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list7 = []\n",
    "positive_list7 = []\n",
    "negative_list7 = []\n",
    "neutral_list7 = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweets7 = api.user_timeline(target_user7)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets7:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results7 = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results7[\"compound\"]\n",
    "        pos = results7[\"pos\"]\n",
    "        neu = results7[\"neu\"]\n",
    "        neg = results7[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_list7.append(compound)\n",
    "        positive_list7.append(pos)\n",
    "        negative_list7.append(neg)\n",
    "        neutral_list7.append(neu)\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_user7}\")\n",
    "print(f\"Compound: {np.mean(compound_list7):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_list7):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_list7):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_list7):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "msnbc_news = pd.DataFrame(\n",
    "    {'Compound': compound_list7,\n",
    "     'Positive': positive_list7,\n",
    "     'Neutral': neutral_list7,\n",
    "     'Negative': negative_list7\n",
    "    })\n",
    "msnbc_news[\"Tweets Ago\"] =  range(len(msnbc_news))  \n",
    "msnbc_news[\"name\"] = \"MSNBC\"\n",
    "msnbc_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @abc\n",
      "Compound: -0.037\n",
      "Positive: 0.057\n",
      "Neutral: 0.872\n",
      "Negative: 0.071\n"
     ]
    },
    {
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       "      <th>3</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound  Negative  Neutral  Positive  Tweets Ago name\n",
       "0    0.0258     0.136    0.723      0.14           0  ABC\n",
       "1    0.0000     0.000    1.000      0.00           1  ABC\n",
       "2    0.0000     0.000    1.000      0.00           2  ABC\n",
       "3    0.0000     0.000    1.000      0.00           3  ABC\n",
       "4    0.0000     0.000    1.000      0.00           4  ABC"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target User Account\n",
    "target_user8 = \"@abc\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list8 = []\n",
    "positive_list8 = []\n",
    "negative_list8 = []\n",
    "neutral_list8 = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweets8 = api.user_timeline(target_user8)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets8:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results8 = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results8[\"compound\"]\n",
    "        pos = results8[\"pos\"]\n",
    "        neu = results8[\"neu\"]\n",
    "        neg = results8[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_list8.append(compound)\n",
    "        positive_list8.append(pos)\n",
    "        negative_list8.append(neg)\n",
    "        neutral_list8.append(neu)\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_user8}\")\n",
    "print(f\"Compound: {np.mean(compound_list8):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_list8):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_list8):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_list8):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "abc_news = pd.DataFrame(\n",
    "    {'Compound': compound_list8,\n",
    "     'Positive': positive_list8,\n",
    "     'Neutral': neutral_list8,\n",
    "     'Negative': negative_list8\n",
    "    })\n",
    "abc_news[\"Tweets Ago\"] =  range(len(abc_news))  \n",
    "abc_news[\"name\"] = \"ABC\"\n",
    "abc_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @newsweek\n",
      "Compound: -0.236\n",
      "Positive: 0.031\n",
      "Neutral: 0.848\n",
      "Negative: 0.121\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Tweets Ago</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>NewsWeek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.2500</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.087</td>\n",
       "      <td>1</td>\n",
       "      <td>NewsWeek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
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       "      <td>2</td>\n",
       "      <td>NewsWeek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.4019</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>NewsWeek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4</td>\n",
       "      <td>NewsWeek</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound  Negative  Neutral  Positive  Tweets Ago      name\n",
       "0    0.0000     0.000    1.000     0.000           0  NewsWeek\n",
       "1   -0.2500     0.136    0.777     0.087           1  NewsWeek\n",
       "2    0.0000     0.000    1.000     0.000           2  NewsWeek\n",
       "3   -0.4019     0.252    0.748     0.000           3  NewsWeek\n",
       "4    0.0000     0.000    1.000     0.000           4  NewsWeek"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target User Account\n",
    "target_user9 = \"@newsweek\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_list9 = []\n",
    "positive_list9 = []\n",
    "negative_list9 = []\n",
    "neutral_list9 = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweets9 = api.user_timeline(target_user9)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweets9:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        results9 = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = results9[\"compound\"]\n",
    "        pos = results9[\"pos\"]\n",
    "        neu = results9[\"neu\"]\n",
    "        neg = results9[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_list9.append(compound)\n",
    "        positive_list9.append(pos)\n",
    "        negative_list9.append(neg)\n",
    "        neutral_list9.append(neu)\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_user9}\")\n",
    "print(f\"Compound: {np.mean(compound_list9):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_list9):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_list9):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_list9):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "newsweek_news = pd.DataFrame(\n",
    "    {'Compound': compound_list9,\n",
    "     'Positive': positive_list9,\n",
    "     'Neutral': neutral_list9,\n",
    "     'Negative': negative_list9\n",
    "    })\n",
    "newsweek_news[\"Tweets Ago\"] =  range(len(newsweek_news))  \n",
    "newsweek_news[\"name\"] = \"NewsWeek\"\n",
    "newsweek_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: @washingtonpost\n",
      "Compound: -0.092\n",
      "Positive: 0.068\n",
      "Neutral: 0.816\n",
      "Negative: 0.116\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Tweets Ago</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.2960</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2023</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.189</td>\n",
       "      <td>4</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.5423</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.231</td>\n",
       "      <td>5</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.310</td>\n",
       "      <td>6</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.7430</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.4019</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.4939</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.211</td>\n",
       "      <td>9</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.3818</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.179</td>\n",
       "      <td>12</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.4588</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.000</td>\n",
       "      <td>13</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.6249</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.5584</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.110</td>\n",
       "      <td>15</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.5267</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.1280</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.120</td>\n",
       "      <td>17</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.2960</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>19</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>20</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>21</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>22</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.2960</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.2023</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.189</td>\n",
       "      <td>24</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.5423</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.231</td>\n",
       "      <td>25</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.7430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.310</td>\n",
       "      <td>26</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.7430</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.4019</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.000</td>\n",
       "      <td>28</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.4939</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.211</td>\n",
       "      <td>29</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>-0.3818</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.000</td>\n",
       "      <td>70</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>71</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.179</td>\n",
       "      <td>72</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>-0.4588</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.000</td>\n",
       "      <td>73</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>-0.6249</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.000</td>\n",
       "      <td>74</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>-0.5584</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.110</td>\n",
       "      <td>75</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>-0.5267</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.000</td>\n",
       "      <td>76</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.1280</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.120</td>\n",
       "      <td>77</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>-0.2960</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.000</td>\n",
       "      <td>78</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>79</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>80</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>81</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>82</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>-0.2960</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.000</td>\n",
       "      <td>83</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.2023</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.189</td>\n",
       "      <td>84</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.5423</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.231</td>\n",
       "      <td>85</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.7430</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.310</td>\n",
       "      <td>86</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>-0.7430</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.000</td>\n",
       "      <td>87</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>-0.4019</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.000</td>\n",
       "      <td>88</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.4939</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.211</td>\n",
       "      <td>89</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>-0.3818</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.000</td>\n",
       "      <td>90</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>91</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.179</td>\n",
       "      <td>92</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>-0.4588</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.000</td>\n",
       "      <td>93</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>-0.6249</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.000</td>\n",
       "      <td>94</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>-0.5584</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.110</td>\n",
       "      <td>95</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-0.5267</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.000</td>\n",
       "      <td>96</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.1280</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.120</td>\n",
       "      <td>97</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-0.2960</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.000</td>\n",
       "      <td>98</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99</td>\n",
       "      <td>Washington Post</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Compound  Negative  Neutral  Positive  Tweets Ago             name\n",
       "0     0.0000     0.000    1.000     0.000           0  Washington Post\n",
       "1     0.0000     0.000    1.000     0.000           1  Washington Post\n",
       "2     0.0000     0.000    1.000     0.000           2  Washington Post\n",
       "3    -0.2960     0.196    0.804     0.000           3  Washington Post\n",
       "4     0.2023     0.140    0.671     0.189           4  Washington Post\n",
       "5     0.5423     0.000    0.769     0.231           5  Washington Post\n",
       "6     0.7430     0.000    0.690     0.310           6  Washington Post\n",
       "7    -0.7430     0.296    0.704     0.000           7  Washington Post\n",
       "8    -0.4019     0.270    0.730     0.000           8  Washington Post\n",
       "9     0.4939     0.000    0.789     0.211           9  Washington Post\n",
       "10   -0.3818     0.206    0.794     0.000          10  Washington Post\n",
       "11    0.0000     0.000    1.000     0.000          11  Washington Post\n",
       "12    0.3400     0.000    0.821     0.179          12  Washington Post\n",
       "13   -0.4588     0.273    0.727     0.000          13  Washington Post\n",
       "14   -0.6249     0.194    0.806     0.000          14  Washington Post\n",
       "15   -0.5584     0.290    0.600     0.110          15  Washington Post\n",
       "16   -0.5267     0.274    0.726     0.000          16  Washington Post\n",
       "17    0.1280     0.000    0.880     0.120          17  Washington Post\n",
       "18   -0.2960     0.186    0.814     0.000          18  Washington Post\n",
       "19    0.0000     0.000    1.000     0.000          19  Washington Post\n",
       "20    0.0000     0.000    1.000     0.000          20  Washington Post\n",
       "21    0.0000     0.000    1.000     0.000          21  Washington Post\n",
       "22    0.0000     0.000    1.000     0.000          22  Washington Post\n",
       "23   -0.2960     0.196    0.804     0.000          23  Washington Post\n",
       "24    0.2023     0.140    0.671     0.189          24  Washington Post\n",
       "25    0.5423     0.000    0.769     0.231          25  Washington Post\n",
       "26    0.7430     0.000    0.690     0.310          26  Washington Post\n",
       "27   -0.7430     0.296    0.704     0.000          27  Washington Post\n",
       "28   -0.4019     0.270    0.730     0.000          28  Washington Post\n",
       "29    0.4939     0.000    0.789     0.211          29  Washington Post\n",
       "..       ...       ...      ...       ...         ...              ...\n",
       "70   -0.3818     0.206    0.794     0.000          70  Washington Post\n",
       "71    0.0000     0.000    1.000     0.000          71  Washington Post\n",
       "72    0.3400     0.000    0.821     0.179          72  Washington Post\n",
       "73   -0.4588     0.273    0.727     0.000          73  Washington Post\n",
       "74   -0.6249     0.194    0.806     0.000          74  Washington Post\n",
       "75   -0.5584     0.290    0.600     0.110          75  Washington Post\n",
       "76   -0.5267     0.274    0.726     0.000          76  Washington Post\n",
       "77    0.1280     0.000    0.880     0.120          77  Washington Post\n",
       "78   -0.2960     0.186    0.814     0.000          78  Washington Post\n",
       "79    0.0000     0.000    1.000     0.000          79  Washington Post\n",
       "80    0.0000     0.000    1.000     0.000          80  Washington Post\n",
       "81    0.0000     0.000    1.000     0.000          81  Washington Post\n",
       "82    0.0000     0.000    1.000     0.000          82  Washington Post\n",
       "83   -0.2960     0.196    0.804     0.000          83  Washington Post\n",
       "84    0.2023     0.140    0.671     0.189          84  Washington Post\n",
       "85    0.5423     0.000    0.769     0.231          85  Washington Post\n",
       "86    0.7430     0.000    0.690     0.310          86  Washington Post\n",
       "87   -0.7430     0.296    0.704     0.000          87  Washington Post\n",
       "88   -0.4019     0.270    0.730     0.000          88  Washington Post\n",
       "89    0.4939     0.000    0.789     0.211          89  Washington Post\n",
       "90   -0.3818     0.206    0.794     0.000          90  Washington Post\n",
       "91    0.0000     0.000    1.000     0.000          91  Washington Post\n",
       "92    0.3400     0.000    0.821     0.179          92  Washington Post\n",
       "93   -0.4588     0.273    0.727     0.000          93  Washington Post\n",
       "94   -0.6249     0.194    0.806     0.000          94  Washington Post\n",
       "95   -0.5584     0.290    0.600     0.110          95  Washington Post\n",
       "96   -0.5267     0.274    0.726     0.000          96  Washington Post\n",
       "97    0.1280     0.000    0.880     0.120          97  Washington Post\n",
       "98   -0.2960     0.186    0.814     0.000          98  Washington Post\n",
       "99    0.0000     0.000    1.000     0.000          99  Washington Post\n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target User Account\n",
    "target_userz = \"@washingtonpost\"\n",
    "\n",
    "# Variables for holding sentiments\n",
    "compound_listz = []\n",
    "positive_listz = []\n",
    "negative_listz = []\n",
    "neutral_listz = []\n",
    "\n",
    "# Loop through 10 pages of tweets (total 100 tweets)\n",
    "for x in range(5):\n",
    "\n",
    "    # Get all tweets from home feed\n",
    "    public_tweetsz = api.user_timeline(target_userz)\n",
    "\n",
    "    # Loop through all tweets\n",
    "    for tweet in public_tweetsz:\n",
    "\n",
    "        # Run Vader Analysis on each tweet\n",
    "        resultsz = analyzer.polarity_scores(tweet[\"text\"])\n",
    "        compound = resultsz[\"compound\"]\n",
    "        pos = resultsz[\"pos\"]\n",
    "        neu = resultsz[\"neu\"]\n",
    "        neg = resultsz[\"neg\"]\n",
    "\n",
    "        # Add each value to the appropriate list\n",
    "        compound_listz.append(compound)\n",
    "        positive_listz.append(pos)\n",
    "        negative_listz.append(neg)\n",
    "        neutral_listz.append(neu)\n",
    "\n",
    "        \n",
    "        # Print the Averages\n",
    "print(f\"User: {target_userz}\")\n",
    "print(f\"Compound: {np.mean(compound_listz):.3f}\")\n",
    "print(f\"Positive: {np.mean(positive_listz):.3f}\")\n",
    "print(f\"Neutral: {np.mean(neutral_listz):.3f}\")\n",
    "print(f\"Negative: {np.mean(negative_listz):.3f}\")\n",
    "\n",
    "# Convert sentiments to DataFrame\n",
    "washpost_news = pd.DataFrame(\n",
    "    {'Compound': compound_listz,\n",
    "     'Positive': positive_listz,\n",
    "     'Neutral': neutral_listz,\n",
    "     'Negative': negative_listz\n",
    "    })\n",
    "washpost_news[\"Tweets Ago\"] =  range(len(washpost_news))       \n",
    "washpost_news[\"name\"] = \"Washington Post\"\n",
    "washpost_news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create list of all dataframes\n",
    "all_dfs = [bbc_news, cbs_news, cnn_news, fox_news, nytimes_news, economist_news, msnbc_news, abc_news, newsweek_news, washpost_news]\n",
    "\n",
    "# Give all df's common column names\n",
    "for df in all_dfs:\n",
    "    df.columns = ['Compound', 'Negative', 'Neutral', 'Positive', 'Tweets Ago', 'Name']\n",
    "\n",
    "#create new dataframe that combines all dataframes    \n",
    "Major_News_Sentiment = pd.concat(all_dfs).reset_index(drop=True)\n",
    "Major_News_Sentiment.head()\n",
    "Major_News_Sentiment.to_csv(\"MajorNewsSentiment\", encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compound</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Tweets Ago</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABC</th>\n",
       "      <td>-0.037490</td>\n",
       "      <td>0.07075</td>\n",
       "      <td>0.87225</td>\n",
       "      <td>0.05695</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBC</th>\n",
       "      <td>-0.139685</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>0.85470</td>\n",
       "      <td>0.03710</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CBS</th>\n",
       "      <td>-0.139685</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>0.85470</td>\n",
       "      <td>0.03710</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNN</th>\n",
       "      <td>-0.140120</td>\n",
       "      <td>0.08740</td>\n",
       "      <td>0.86480</td>\n",
       "      <td>0.04785</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox News</th>\n",
       "      <td>-0.222300</td>\n",
       "      <td>0.13460</td>\n",
       "      <td>0.83260</td>\n",
       "      <td>0.03280</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSNBC</th>\n",
       "      <td>-0.155805</td>\n",
       "      <td>0.07625</td>\n",
       "      <td>0.88650</td>\n",
       "      <td>0.03725</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York Times</th>\n",
       "      <td>-0.291140</td>\n",
       "      <td>0.12720</td>\n",
       "      <td>0.84295</td>\n",
       "      <td>0.02985</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewsWeek</th>\n",
       "      <td>-0.236345</td>\n",
       "      <td>0.12115</td>\n",
       "      <td>0.84750</td>\n",
       "      <td>0.03135</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Economist</th>\n",
       "      <td>-0.036565</td>\n",
       "      <td>0.10410</td>\n",
       "      <td>0.81165</td>\n",
       "      <td>0.08420</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Post</th>\n",
       "      <td>-0.091900</td>\n",
       "      <td>0.11625</td>\n",
       "      <td>0.81625</td>\n",
       "      <td>0.06750</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Compound  Negative  Neutral  Positive  Tweets Ago\n",
       "Name                                                              \n",
       "ABC             -0.037490   0.07075  0.87225   0.05695        49.5\n",
       "BBC             -0.139685   0.10820  0.85470   0.03710        49.5\n",
       "CBS             -0.139685   0.10820  0.85470   0.03710        49.5\n",
       "CNN             -0.140120   0.08740  0.86480   0.04785        49.5\n",
       "Fox News        -0.222300   0.13460  0.83260   0.03280        49.5\n",
       "MSNBC           -0.155805   0.07625  0.88650   0.03725        49.5\n",
       "New York Times  -0.291140   0.12720  0.84295   0.02985        49.5\n",
       "NewsWeek        -0.236345   0.12115  0.84750   0.03135        49.5\n",
       "The Economist   -0.036565   0.10410  0.81165   0.08420        49.5\n",
       "Washington Post -0.091900   0.11625  0.81625   0.06750        49.5"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mns_group = Major_News_Sentiment.groupby('Name')\n",
    "mns_group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NsoZyMDDd9jrA9Ho83CxgK9ubAJsDB0tatV47EtgXWKd+var/IUdERCeDTCg7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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb839b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split up our data into groups based upon 'name'\n",
    "news_names = Major_News_Sentiment.groupby('Name')\n",
    "\n",
    "# Find out how many of each gender took bike trips\n",
    "news_score = news_names['Compound'].mean()\n",
    "\n",
    "# Chart our data, give it a title, and label the axes\n",
    "news_chart = news_score.plot(kind=\"bar\", title=\"Major Network Tweet Sentiment\")\n",
    "news_chart.set_xlabel(\"News Network\")\n",
    "news_chart.set_ylabel(\"Compound Sentiment Score\")\n",
    "\n",
    "fig.savefig('AggregateTweetSentiment.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KGTXqfSul7C2lPLP8/39sf9MmEt6qC6LKL88qY4MoTctglZXhxAWCsMTOQZSm+elx5mAU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\nzFn7we+to95wJfG4a3/XAqXWWxcVJgDSCFNaXU52WmYzd3UO9ihQpJT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m2/l185qoByGY\nz3jQsaOZ8LfxzdzVOZBxMSjxBVqddgd9esTvwhrCMGLvUxSVPj0PahEPQggmTJjAM8+YecemTZuG\nx+NpMknkvuD2228nJT2VK68xF3rXXHE93bp3Zdr0GQDccccd9OjRgwkTJpiOBy1wPtiFxMREPB7P\nngn3Ei3J5dVPCPGsEOI/QohPdv21G0f7KSyu+upoAsFBA4d0Ije7R0LWwdF6IAAOuXdV3U4aehJK\ng/WGxZqyXwoTAKeREf0sUEjpEZ+qpSVwpMV6+aR27bMbys7FwYcfhyLrvYbsWjLnnnB6M3c0jVHD\nTkFV61V5quLgzONb305HQIhYLymhtFx11BBWa6x6d3dek03Bbrfzn//8J2432NY44YQTWLN6DQKB\nYRhUV1Wzbct2kiPBkMuXL2fkyPjg1/0BLVF5fQTsBJ7H9Pja9bfXEEKkCyG+EkJsjfyf1gTNECHE\nCiHEeiHEGiHE3xpce1MIkSOEWB35a/fZ/eFbnyExexBCtXHosNH7bUr45+6aQWbGn0g0epEkDsWa\n1Itxd/6Z6yZdxNuLPmhxO2cP/xMnnHE1jpS+KBY7/7zukXbket9w502vkqwcSqLRi9TEEdQVb2Xc\nnX9m/MNjW1XM6LlJz5HRexhCtdFt4Ek8ceOje76pE3DHmBvpN+ASEo3eJDGAxC5DGHfnuVw3cTRP\nvtPycsc9M7tx6WWTcKb2R6g2Rl98y35bnCsjLQtVOFGxYREukJKd+VvJLdyBv4lock+Vj20/5lOw\nvjSmcmn3zG7YHEkI1YYQCj2yeraYB4vFwnXXXcf06fFOruXl5Vx88cUMHz6c4cOH8/33ZoWPwYMH\nU1NTg5SSjIwM/v1vc8d75ZVXsmjRItavX8+xxx7LkCFDOPLII9m6dSsjR47kx5UrSUrKZvuWXA45\n5BASE5PJzdnC5m3r2bBhA0cffTQATz/9NMOHD+fII4/kwQfr09e8/fbb0Xavv/56dD12Z1ZRUcGI\nESNYuHBhi8ffou+oBTQBKeXMNu0VJgNfSymfEEJMjhxPakTjA66SUm4VQnQHVgkh/iel3OWKMlFK\n2fIZch/hDfvQw0GQkuq6Km6eeiNawE1mr0OarOVQ7a7hoRcfJOStJiW7T4dOTs/fPQuAyS/cR3lO\nJFOvFmbpV++2Si/uCXgx9DBSSj799lNemfMUQlG55IK/N5keZO7iBSz71ty8jjrjEi486c/7PpgW\nYNCAgbz61EcAXD/54ojdB4J1eTz02mM8dcvUFrXj8/kIhQMgJV6vm9un3UGgrpyEtK5Mu73pqtd3\nzbwHd0XBXtWq2RdMHf8QALM+foNflr5rnpSw7ZcvKDv7crIyWqZLr/W5Te8mKVm9eS2ffTEXpMHw\n489uMr5qxcZVvPXeS0hD26taNfuCvj1Nx5TcwhykYXopSkOjrLIkJujRW+3nu7d/JRw0aaqL6hg8\n6uDoddMGagqZ4ooSdC2EUFS6ZXaLy68FUFRRTDgYQErJ2H+M5djhx3LXXbFuzrfeeiu33347J554\nInl5eZx11lls3LiRkSNH8v3339OnTx/69+/PsmXLuOqqq/jhhx946aWXuPvuu7n11lu5/PLLCYVC\n6LqO0+nEYrHgr/ORu6OIU049ja1bN/HLL7+SlJTIIYcOxB3wsurbb9m6dSs//vgjUkpGjx7N0qVL\nyczMZP78+Xz//fdYrVbGjx/PO++8w4UXmsXXSktLGT16NI899hijRrVt0b2WCJTnhBAPAl8C0XBe\nKeUv+9DvBZgGfoA5wLc0EiiRJJS7PhcJIcqATGD3zvTtiKdeuIeQvxTZZyi5mxdgJQlFsVO0sZgn\n30mMc6+8b8YkAjVm2uzyHcXc89KDTP3nwx3Ks88T+1VJveUJDhYs+4zfvn8fKXUMPcjGde/gEKaR\nc+67T3PcwKNjJs/VOzbwzcLXMKTpevn5Jy9xeJ9D2szVuaUwGo3R763dDWU8Jj03EV/FFmTXQVTm\nL0GgYlESCXmLmfDsnTw7YVoM/V0z76Ey9ycAQt5iJs2YyPP3zNr3QbQCpWWxLt8GGtvLclskUHJK\n8li4YBaGEUAaITb+Ngc7GQihsvzrf3Nwr/5xruFvvDklKrDX/rCABdm9OmzhEIU0mj0u3VYZFSYA\nhRvKogKlsKwILWjaFaU0CIVqUbAgjTBFZYVx0filVWWE/PW2NX/Iw1VXXcXMmTNxOuvVhYsWLWLD\nhvo0MXV1dbjdbk466SSWLl1Knz59+Oc//8ns2bMpLCwkPT2dxMRERowYwZQpUygoKOCiiy7i4INN\nPkeOHMny5ctZvnw5Y6//B0lJTlat+oWkpCSGDj2acDjEl19+yZdffhndrXg8HrZu3cqaNWtYtWoV\nw4ebVeb8fj9ZWeZvNxwOc/rppzNr1ixOOeWUVn/1e0JLVF6DgXHAE9Sru6Y1e8eekS2lLAaI/B/v\njtEAQohjARtmtP4uTImowqYLIdo9yU9DY7euhNAxt9lSQEHuljj6sCfWI6OmpOmgqQ3bt/DkuzPZ\nsD2+jX3F0UedGKN7tiW3PBL45/U/NfBxl2hKMHqs615Wbvk1hn7JL8uiwgRANwIsW/tDk22//eV8\nXvn4zRbz0hrYk+tdXQUqQ448ocX3hmrrw6t0EYw+YwBfVXEcvbuica2apsOzdhbl8cy8WXy37ucm\nr+8LLjj1PFSl3n3WYs9ocYT7t78uj8YXSYg8Y3MiNmSYZb/GFmZdl7s5KkwApNT5aTfPeOHyr3lm\n3qx2sTeo1lh3YYst9tiRZN/tsRYTayKhgXODbKLgVbCROk0aGrfddhuvv/46Xm+9w4thGHy08GMW\nL/uW1atXU1hYSFJSEieffDLLli1j2bJlnHrqqWRmZvLBBx9w0kknAXDZZZfxySef4HQ6Oeuss/jm\nm28A046yfPly1q5dy3HDjuWYY47h11Wr+WXVrwwdPpTUxBSklNx9992sXr2a1atXs23bNsaOHYuU\nkquvvjp6fvPmzVHHAYvFwtChQ/nf//7X/Je8lxAN9YtNEgixCThSStmqqB8hxCKgaxOX7gXmSClT\nG9BWSynj7CiRa90wdzBXSyl/aHCuBFPIzAa2SymbVPILIa4DrgPIzs4eOm/evNYMI4q8wh1IqZPo\nSqPOV4ZARUTksdWRSPcusW60eUU7kUY4eqzaXPTMivXrL64oIRCoQiIRCJzODLq2wmOjJaisq8bn\n86CoKt0yuqIoLVlDmPd56szJYNeYTSO96aLYs3u/mHQSnoCPyorYyOSsrF44G/3Ycwu3o0vzR6pi\no0/Pg2lrFJYXYeg6TmdCq8qm5hXnIvWQOV5vOUIIBOYYFYuDXl1jY2/ySwswwvXBdEK10btbrCG/\nxlNLdXURUhgIBBZrEr3aOIbH7fNQ465GCEF2enaTapumEAgFKS3LB2QTzxjS0ruS7IotU5tbsI2G\nk3BichcykmN/unnFuWi66UmkSJWuWf1w2PdtzZeSksJBB9V7Y4W0sOnmLEST4928NJeijeXYE6wc\ncdZBJGeacUehcAgZ2dGoiopuaNHxgsBui+UzpIWREc+wPn36kpubi91m5/777+fDDz/kiiuuYPLd\nk/n7tX9n8OAjuOnmGwGFzZs2c+SRRwJw9NFHk5yczJIlS5g+fTqzZ89m2rRpnHvuueTk5NC3b1+E\nEEyaNInevXtz4403smbNGq644gr69u3LJ598gmEYnHzKyZQUl7B8xQqyMjP5+uuveeyxx/jvf/9L\nYmIiRUVFWK1WKisrGTNmDF999RWZmZlUVVXh8Xjo0aMHPXv2pKCggKuuuoqhQ4dGq0s2xLZt26it\njd3Zn3baaauklHtM39wSlddvQCqtjI6XUp6xu2tCiFIhRDcpZXFEODTZthAiGVgI3LdLmETa3rVc\nDAoh3gB2GyQgpZyNKXQYNmyYPPXUU1szjCi+W+finfdeYMQhp/Jr4UbC3jqMkI+ErD5xqhAwg+Rm\n/GsKesCNPTWbJ255Kk6/fu3tJ+NT64eeoHfjX9MX7xV/7YEHX3+ckm2/MnLwOawp3YGvIg+hWDjx\ntIs4/U/x3kDPzn+Rzb8tBeCI4aO4+sxYN93XF77LV79Oi3F57VF2Ps9OeLp9B9JCFJQXM/XF+zn+\noBP4ceevYOgY/jqsSRk8essTcYGWPp+PSTMmEqorQ3WmMPH6B+KCE8fdeT511Efd27RE3prR9juV\nvcWsj99gzcr/MfLwM1lbvhNfeT5SGvQ/4gSu/vP5cfTvL/Hw9ZdzkUaY7P5H8cgV4+JoLpswGF2p\nX0yl2o7mlalz94nPjRs3kpTU8hrsw849As5t+lpecR5SD5PgTMEX8ps7E6GQ2aVrk0WvzIVDECTY\nXU6SnAncfffdzJ49G7vdTo27iskP3MZD903hxBPnoms6p512OrNnzwbMjMO6rpOUlMQZZ5zBQw89\nxKhRo0hKSmLhwoW8/fbbWK1WunbtymOPPUZSUhLHH388lZWVXH755dFxHz3kaFb4VzCgv+ka/Ze/\n/IXc3FzOPNNM1ZOYmMjbb7/N8OHDmTp1KhdddBGGYWC1Wpk1axaqai6OUlNT+eCDDzj//PPJzMyM\nKxLmcDiiarTWoiU7lG+BI4GfiLWhjN6rHs02nwYqGxjl06WUdzWisQGfA/+VUs5odG2XMBKYecUC\nUsrYYgVNYNiwYfLnn/ftx/ztt9+yO6FUVlnBY7MfIuSrI71bX6aOj980+Xw+7nnxXgK1ldS51xCy\n1yGFjjBUksI9eWP60mb7v/7eKwj58rFYUrj39hfo2z0+0viBV6dQnr8ZiyOBSeMe2GMQ4lPvziRn\n0yoUq52/j7mRoQcf1eIx//t/7/HD8s8AwZln/a3JWI+Pln/Bl/+bTzhQS41cg6FoICSqZiM74yye\nb5SqvSHmf72ALxe+hJQhErsOZmYTsT9b8rYz862n0YM+uvU/nAeuaezfEQvTYeJ+gp5aUrvGO0w0\nN16AO6fzFqyRAAAgAElEQVTfhaeqBEdKhlkArQlD/D0vPkBV8U787q14baVIRQep4Ail8s70X+Mb\nbYCbpo7HV74eRXFy4cUTm3QJfu6DV9i0ZjlCtXLZxdftMQ3O24s+YPmyTwHRpMNEc2NevPp73v/4\nX0hD54hjTuHGC+JD01bv2MCr78zACAep9K1At4QAiWJYSVePaTaR54btW5j+0h3oeh32pL7RhJAN\nsX79epIykpC6jmqx0jN7z95ZBaUF6FoYoar0yOwRnVB3we1271ZIFZYXo4UCCEUhu0tX7Nb4HVZx\nZSmhgA9phNAJIYU5lwqp0DP74LjdTgxvZUVoQVN96EhIo2sTQZjV7lrq6kxVe2JSChl7SGXv9nup\nqi4HKXE4E8lOjw0ZaG68DbFx40YOO+ywmHNCiBbtUFqi/3gQuBCYShu5DWPaY0YJIbYCoyLHCCGG\nCSFei9D8H3AycE0T7sHvCCHWAmuBLkD7Ze5rBR56YRLeis2EfcWUbl/BA69OiaOZ/NxduIvXEfYV\nI0UYzeZHt2jo1hBWa7OmJG6470pqgj/jU0upk1t47Jn42tWPvzWdoo3fEvIW46vcxpTn6+Xsd+t+\n5J6XHmTa3PoJ/I3P57Fl1aeEvEUEanKY/a94nneHFRtXseyrNwl6Cgl6Cli4YBYF5bG2hl2G36Cn\nAD1ch24JoltC6KopVM444Zxm+1j438fxKHl41RLKyr7mvpfjveWenf0A/qrthLzF5K1ZxLPz68v1\nPPfBK9zz4gN88dM30XP3TZ+Ip2wTYV8x5Tt+4N6XW+4sccf0iVQX/ErYV4y7eB2TZ8YnNbzvlUco\n3b6CsK8YnRCa3XzGhjWEnebTyUx87h4qqhbjVUtxi528/158FoYFyz5j7YoFhLxFBOty+fdbT0Sv\n5ZTkcd8rj/DQ609EXaZXbf2NJV+8EX1On3/yEjm7sek1hs/nY+7caQTr8gh5Cvl16Xw+/eGrOLqX\nXn2IQE0OIW8RhqKhW4PmmBWN9O7/j733jpOi2N7/39U9aWdzJEdJIkkwXcSAmMWcE17FABgQ9SoG\nFBEVFAWUzEWigoELKChekSBRkqBkJO+yOe9O7q7fHz07s7MzmwD1/r4fH1+83J3t6p7q6q5Tdc5z\nnnN2hDMHMWbiIEo4SLmaTYHjZwa+GU7JLyotQve6kLoXn8dBenYwfpVfUkB6Tgb5JcE4Z3p2Oj6P\nA6l70b0uMnLSw85ZHbLys/G6So22PjdZOeFad7lF+bgdxUjdi5Q6UtENvRohEVKp0ZjkFuXjcRei\n4UXDi6M8F6crNFbj9ropLsoxzq97KS3Ox+E23KuapnEyN5OM3JO4vUESSn5+JlLzIHUvzvJCCkvr\nTkg5U6hLPZQ1wD4g1v9vr/+zU4aUMl9K2UdK2db//wL/51ullI/6f54npTRLKbtV+rfD/7crpJSd\npZSdpJQPSCn/uNTParB4w3I+/HxSiCKrrzy0ql5+5tGwdu5KgVsFE9byRGyOBGLLm6LU8BACIbXl\nAdxaLh9+Pilksjx54kBIhrvPaXynFdvXMnv2CLJ/38DerUt4ZpQhx77nwI7QrGt3YURV2sLSIsZ/\nNZWZ3wVjUGt/WY8ug64NXXexdldokHbtzk3BwK/0YfZEYytPJKo8iWhPc/ZEIDRU4OjJ47hNJYHf\npdDJPrSdsV9OCTFcWiXChBRw7KhRa+P5sf/i141fkX1oIws/H8P8VYuMe1JlnAqzI0+u63Zt5sPP\nJ7GxkpCkIz/UYLqLc6s2oyDrWOBnIQRWRzw2RwIxZY0xmWteIRad3BcyHi61kLFfTgl8d4Atu34O\nyXDXfKXsOraf9NxM3h37LJkH1nJiz4889+4AAH7asTGMMLH6lw0Rrz9t6Vw+XjQjYIy2H94V0JMC\nQwz1519/DmnjcDjQPMFnxqRbsZUnEeVIJM7ZApe7lJrgJjRw7y46yoefT2LH4SBrSlbJcNd8xnOX\nmZ9NaXEePnc5pcV5ZOZnh/w90D5CwB0MjbCsghyKy4PPmccTHoivCpc7NL9J6AqKbkLRzCjChLfK\n9UOu6SwLGWOJJLcwh9yi4HNZUl5G5VgVSErLjfuYnnUMj6sUr6uMzOwTeLwew9hUYbuVO//0abFO\nmfJ3AZsxRCHvAn4WQvzviPz8BXhl8hssXfghuzcvYsyEIazbtRkA1RYqEhmTGL7jMNmDK1RdaLij\nC3HZiyiNzkCLUAK0Msy2YEa4FDpe1cnuzYv4asFoRswaDUBCSmjgX7UY32nZykURizg1b94WUem5\nVc2xYbECTdcY+u4Aft34FetWzODpdwYC0L3DuSEsMkVY6HFWl5C23Tt0RfHX9xCoeC3luGIKcUYX\nUG4+QauGLavtb8vGzTFrQRFHXfHi9mSxa9NCRox5MmDMFXPofU/xu0NKT4YWcdqw8b9GH6uMU3RC\nuJrAx4tmMHvmG+zevIhPZgwLlCiwxIYGoM328B1HTGKQWCEBt70Yl72IsuhMfN6aJ9eopCYBpQOJ\nRFd87Nq0kB+/mcSQMYaMf9uWZ4eoIahKFJ1atGfWt5+FsLC8jkxWbF/L+Wd3D5ALwJDIP79juI98\n0JuP8vOqOexYt4Dn3nkMh8PBOS3aoVRikQkEHdueE9LObrejmIJ1OHyKG1d0AU57IaVRxzCbas7N\nscjgeOiKDw+F7N68iImTX2TxhuX+C4dOVcKv1utxhUoLVfwuqri3iFCPRNM1snNO4CovorAgiwz/\nIkWtWoNIhLe1hLjABFLR0RUfuupFx1cjMcJaNU4jJJrXSXlpPsdOHgXwF9YKFau0R0VTUl4aauCk\nTn5JIVaTJex4W4R40B+Nuri8XgXOl1I+JKXsB1wA/O+oIf4FyDv8S2CFoWlOvlw2F4An+7+KNaYp\nJnMCMQ06MvrpcPfRywPexBbfEpM5ASEVFK8VRVdRNBNeT3aN1337+UnEiXZYfXFYvXFE6YbBkuik\n7zVWjW89/hrxTbpiMidgiWnCY/54giXsITZeyKdv7U9a256YLIlYohpw460Dw66bU5iHzx1cPTny\nf2fH4T1c2f0SOvToi8mShNmawvmX3xOWd9KpRXu6X3o3ZmsKimpHeM0oPhOKpiKRbNi+surlQnDu\nhY9g11KxaQnYPAmY/ROX5ivl34tnAPDg/c9jiW6EyZJIcvPz/IWoCFGEBlD8k8Cz/YdhjW1mjFNa\nB94fPIqq2L31R3SCFNotmwya5fBBb2FPboPJnIAtoRVDBwwPa/veM+8Qk9YRkzkBRSqoHpsxxroJ\nt6w5jeqjlz4m3toDmy8eqy8Wu9cgSkoBpZm7cTgcPHzdPTTp2BuTJRGzLY2r+hquz/i4UOMmEKQk\nJNOr0wV0vOAmTNZkTNZkul1yZ1hGfImjFHdJcGfldeYw5ZtZJMYmcMX1j2C2pWGyJNK8cx/u7X1r\n2Pe+7Y6nsEQ1wGRJREgF1WtB0VWQKsXZNVPi77jzDaL1xlh9cdi88VgVY+Gk625++NFQjo6PTfBn\nt6soJmulYlhVFYKN35ukNkExWY26OqqFRhGKZ2maj8q7gIr8lCapjVAtUUZbxUxyBOZlg6Q0TNbo\noLGRIvBPouOuYXHYICkNsykWRRpMUUU3BZSOpeahoLQIuzUKe2yi8R2ESlR0ArFR0ZhM4TwqRVFQ\nVZW4hFSEYkIIFbMttl4MxzOFugTlf5NSdq70uwLsrPzZ/19wOkH5jXu3MXfBx1zU7lJWb51jVJwD\nNM2NRxQbtF+lIaNenkdKSt2ylB8ecgkONegyidYa8MnYNaTnZvL2hJfxOfJRbfEMePiVMB2tp94e\ngLMg6G4zmeOYOmphtdcqLC3i5feewuvIRhEm2na/jhfvq7ke9ohZo8k48As9z7mS1Vvnofj77Nbz\nkegoqCQ27BkxUB4JU5fMYuXa0Mm7Udp1jHtxLB8vmsFvm5aB9JHQ5JyIwoaPPn9tiKsntdWFjHqq\n+vDZxCUz2bFuIbruxmRNZugz79UoEZ+em8moKcO5oPWFrN42HzBoqbruw0M+oGAhngcefIs+3S+t\nU58ffeEGSiulT1m0aOaONVxoT78zEFdhOoopiutufCSM0DB04jByDwddiEKo/HvM8hqv99TbA3Dl\nHwKhkNzyvIiLmsqYtnQu2zd+xz86XsmqLbNQ/XXbvVoJGk6EUImOPoepI+fVqb8A9z7XGb0Syyve\n3I1p7y5g8YblfPfNJ+g+J9aEJhEFSp8Yegc+b9D3H5XQmgnDpkYMFAMUl5dQWJhjuHuEQmJiGvHR\nNZeTOJ51HOnzEh0VR5mjAMMIycACQkgFe0wyDSIEyiPhWMYRfHroTql5w/aYzWYjluN1AoIoe3xY\noDy3KJ/y0lAXbHxiAxL9ul2RkJ6dgc9jXE8xWcOo6tXhfyUov1wI8b0Q4p9CiH9i0Hj/z5X/nTX3\nfdxlGUbmOD40f0a205SDT3XhU12UiqO8+kHN1Q8ro1HbPqi6MUmruoUWnYwJZfT0EXjKMtB1F15H\nNtNmh9Nqb7/hQVTFcCUIVNp27V3jtSqKOL00eArjRyys1ZjMW/EVx3/70diZSB0NB1JKNM2By1yE\nT3XhUcvJzVlZ54S9J27+J3EE806itUaMe3EshaVF/Lr+P2i+UjTNScGxrUz+Opzpk9LqvICrx2RJ\n5Km7a77XT978MOPf/IKXBk9h6jsLaq038u6UN3AWHfZL13vQdSMI6hK5eE3GGDvUbD6bV3cOyOVX\n98OiGeOkSJX41AsBgwXmyP8dXXfh8xTy3dczwto+d89gTBZjlSkQJLeoPWFxwqtTGP7yHN4f/nmt\nxmTXsf1sWb0ArysXpIYUHqT0IXUfDmsuPpMHr+qk2LWN16fXvc/x9m6BHC2rL45+9xus/u++noHP\nU4iuu3AWHOKVSa+HtW3b9fKAKKmqRnH7jf1qvlZ0HC2btqFBWnNaNm1TqzFJz043gvv+JN2KnBQd\nDYluuBmFhqMsv6bThCAtuVGIWKdJRGE2m8ktysfncRgyL1LHWV6Er0osJzUhGaEGXWyKyVqjMQFo\n2qAJTRq1onHDlnU2Jn8W6hKU/xcwFYM63BWYVpXi+38BPlchTpmFhgdderEnteeyqx9D1awhPm+3\n8wRPjniMkbNrJ8K9M2g4/R+YyFmt76H/AxN4o//LxrWqBNM0T7gEfe9uF9Oo3QWYbKlEp7Vn0M2P\n1Kkf7ZqfVSe9qb2/78anl1NOOjo+VKLp0vNOrLFtUfXgC6ApXmZ/+j5PvzMwJHhdHaaP+YYLznua\nTp36B3JuDmcdCwkaSwEZmcfC2j5195NY41tgsqXRsUefOtVksdvtdZZ/8ZUX4tbz0PDglWVY7S3o\ndWV/zFosQgZfFbeWx5MjHmPoxNo9vw9cfTeDH5tF2zb3c8v1I5nwisFAKysOLTOg+8JFLNOSU+h8\n/lWYbGlY41sw4M666WY1TW1Up+JkW/b8gk8ro5wMNDwIaaFBm0tIPetiVC04xjo6J/du4Mm3nggh\nB1SHKSPnclXv12jf/gFGvPgVvTqdF7GPVe8BwKCbH8Ge1g6TLZUGbc6LqBkXCXUtp6tpPmNBiEFr\nFsJETFxKSEIngBQax04e5UTWCXyajyFDhjBuXDB74ZprruHRRx81rm2z8dHYacyZ+SVxsQ1p4dcc\n83irur0krkqusJYtW5KXl0dCfJLhqlLMJCakMGvWLFJTU+nWrRsdO3Zk+vTpYf0wm8w1Fus6evQo\nn332WZ3uyZlEtQZFCNFGCHExGFUZpZTPSSmHAPlCiD9XoOl/AE6ZicNegFQ0SqMz0DUv/a65C7sS\nnNR01YOOhqv4KEd3Lg8xKjOWfcork15n0dpvQ87bp/ulvDNoeIgLJbFxaE2H6NTwlfVbs94nY+9q\nfK5cynL28OLY8IzX04HP56Uk6jiuqBJ0xUe5KZ1n73iCKy6/OWBQJBIpNDRXPo7835k58y1y8g3G\nTmFpEa9Pf5vXp78d+KwCz9/zZDDOgVHm2GQJBrsVYaHPheG5FyPHvYCr+Cg+Vw6/bVxUL/XkusDj\nyqIsOgepaJTFZOFzFfDwdfcQFdMqcIymepGKjqv4KLmHNzFkTPC+L96wnFcmvc60pXNDzntexy6M\nHDCMu/sEYw9nn3N+SGDdHBPup5+/ahE7NnyFz5WDq/gooz+uOb+mvkiLS6bUchxXVLHxXNuO07x5\nK/51z7NYNcM1IpHoqgdNL8dVdJiVy6azYvvawDnenTuWV6e8GcLIAuh/w32MeOy1kDwpc2xQOEMg\naN8xfMf10rjnKcvZg8+VS+a+tQGyyZmC1H3oihepaMbzK32kxCehhFTkNCKkUvOgeZ1kZJ0ISKGU\nOcs5nnWCzKwsdu/eHWixadMmrr/melIrFbiKjY6jspESQiWmSqVQp9tFYUE2UvchdS/5+Zn4NB93\n3303O3bsYPXq1bzyyitkZ9ccX62Kv8qg1JQpPw54JcLnDv/fwtNo/x+GLjRUnxmkwOS1onmM1dWo\nl+fxygdPId0l+LQyFL8PWgrIPGbUIR82bSSZ+39CIll2eDMnC7IjJod9uWYpO/dtp3OHblgsUeSd\nPEJ8amPO73Ihr00dQbuWHUiKT2TTzo3kHt8bQg92FZ7gtakjaN6kFY/3ffC0+1uScwLVa0Wo3kCw\nceG6pdzd51bSc0+yd+u36JoHqfkQivFFNK2cH39dy80XXsPL7w7E6zYMybAPfmPUy1MiZpqPXzgV\nl8fFg/e8wJfLP0XXPFxxyY1s2bONVVtW88A19/D1+u/IL8zD48hC+KVjJBrr137L/sP7uKX3jWHJ\nmKcCHy5Ur8UYY48VXRour6lvf8aA4f3xlWaguV0olWpoOPINuZnJX89m25r5SDRyDm3i6InfI4qB\nrtqxnh9//pEmjVrQtdfd7N+zBas9lhsuu5Fh00aSkpRKr64XsXjVN+T65X4q4HXk8MqUESTEJdTq\nsqwL1u9cj6KbUTUdpEDVzOzbs5OUvg9z952jWLxoPLrmRvM5UPxuGV16WbN1DVd2v4Qn33ocV9ER\nACYd2s6gge9GrJkz/qup5BXkctdN/Vm6ejEuRwntO56PIhRemzqCPhf24WD6YY5nHMFZEKzEKAWk\nH/iF16aO4LYL61cctsSZT3r+QaIsMbRMPSdY3ldqCJQAqaYiGbF5w2Ycz8Jwh6GHhPql7uPiiy/m\n2WefJS/vJPv27aNt29bk5uZTWFiI3W4PxB369OlDQUEBTreLoS8P5eZbbiE75ySDnniS3OxcvD4v\ng58bwiMPPYyma3zwwfus+GEFPq+PiZPHcVab1hSXFVPuLMen+UhLS+Oss87i2LFjmM1mHnnkEQ4f\nPozdbmfatGl06dKFNWvWMHiwIU4rhOCnn35i6NCh7N27l27duvHQQw8xZMiQet2/U0VNBqWllPLX\nqh9KKbcKIVr+Yd/ofxQWPRrp0wxmlm7GFG2sqFNSUpj2rpGbMeC1+/E6g3kmNv8EmnN4Z/ABlhq7\nt/8EVQzKmPkT2Ld1KRKNrAPradv9eiYMm8rkr2ezdOE4JBon961ESh1FteLTy4yJT41G1734KCTz\nwFqy9q/l98N76yzZXh3iUppReNxgZAmpYNJtXNbhIsDYYXDPkyzesJylCz8M9E3BROfWHZm/alHA\nmAD43PksWLWEgTc9FHKNF0YPwl1mTMhzD25l2PMTaJraiCdHPIar+CgA7+7+DiEsCEXFQyEWPQGh\nqHi1YrQSJyfLTjDl4M889NCwWrPFa4NZxGHSSg2Wkm7BpAb98VOGGzGOp995Ekd+kLWk2gzW2W/b\n1wQIAxJJ/rHdVMXiDctZ9p+P0aWHrP1rSWh2LhNencKqHeuZ/+koNN1Fhq7xy/q5qEoUuu5Cw41Z\niUfqGh6KyD64lmzgyUO7mTgsvLxzfdC1fVeOHPoS4VUCz3XrVgb764aefbihZx8KS4t48c37Ay5J\ngaBlk1bk5OfhLjwSWIBruoMF382nWxXVgcHvP0tZlnEvPjv4M3fd+yJXdr+Ef40fSuGxbUgBc/b9\nYGjjKWY8eiEmGY2iWtC0cnzOUjIP5OPofCm5RfmkJiRTG0qdBXy/czZen5FPkleazvln+QkPQgUp\nCP4XdNI092u1pedkBBhfYJQHbty4MYqikJGRwfatv9C9ezeysrPZuHEj8fHxdOnSBbvdzvwF83G4\nSyjIz+O2m+/m8isuZfeOPbRq0YqZs6YhpU5JSQknsw4jpSQhIZavv/2ST+d8zrRpn/Due28aOyPN\nS0bWcTwOjcOHD9OmTRveeOMNzj33XBYvXszKlSvp168fO3bsYMyYMUycOJGLL76YsrIybDYbo0aN\nYsyYMSxduvQUnoxTR00xlJqckn8+wfkvRteLHiLKl4RAIU60Y/Kb4UHjB/xBVCFNWOKa89YTxgq1\ngjNfASkE81ctCknO+33PJnTdh6550HUfR/YYNODftq0KTFReSnErBu3UJKJRhAlVjUYRCmbi/OeG\nohPhk1l9MfqZkcRbz8WsRaNIlWYtbwpjr93S81qadOyNEFYUJYr2591ApxbtSU5MDsltERLKykpC\nMqzX7dqMuyzD6LPuxectYeayeaTnZuIuOuq/URKHKS8gJmkmDhHoswlVMR5DXXexcPnnp93n2+98\nhWitAQKFGL0pQwaGx8Feeuw1zNGNENKEKaoBzz5sbOKVKv5sicr8VYvYdSyYC/Pj6m/QdDe67kWX\nGsUn9wKwZMVXaP7kTw0HLtXY/SqKDUUa/RXChIWggXMXHg5zJdYXd/e5lZSUy7FqsQgUEuMvZvCd\nobTxxNgEul1yB0KJRggzKa0vpP8N9xMTZTcm50rQvBqL1n4bUtTMkX0AqWvGGGtOlq40aMDF6XsC\nO2yXUogPYwI3xlgN9NkkgvktDkfNOTwVyCg4GDAmAEdzgu9DkwYtULH4DYkgLi6cydU0rQnCZAUE\nQqgkJBrMrPMuOI/t235h27ZfOLfHuXTvfi4rV61i9ZrV9OzZEyklLw59iWuvuoEH7nuYrKxscrNO\n0r5De35Y8QPvvj2azT9vJTYuBl0YlOVrr7sGgUKnzp3JOJER+F7LvvmW666+gXvuvYepU6eSlJTE\nunXrePBBw/twxRVXkJ+fT3FxMRdffDHPPfccH330EUVFRRGpxX8WajIoW4QQYcpvQoj+QO3R1//H\n8Pw9TzJr3AZaNT2b6e9HroC8efdWdE8JUvjQyvPZecyYMC678q5AcpgQZrylGaxYOok333s8kLil\na16cSiYOUzZOxSj6A1VLlCrBFZUQWGyNmPbeYmIanIOonLilnpkHaurbnzFv7DZaNuvA6Gcis3xy\n039HSje67uT4UcPFd0vPa4lp3MlYAUoQahT7tn3Noi/fY5BfViMhNh6vVoJTzcShZuHW84mJjiXa\nFhUyUQkEwp/UpihmGne4lGnvLcZsCuXYW+oYlK0JN/Tswydj19Cq6dnM+HAFHc9qF3bMln2/oDkL\nkcKH7i5i/W6jHsrDdw3E5E+wFJjQvaWsWDqJ8R8/y9gvDXqsLiUusnGoWTjUzEDtFkuIHLtAVPJl\nms1JTHtvMU3P6R2gqhs3QzUm9dPExy9PZM7YLbRqejaTXw8P/gL8fnAXaOVI6aUo6xgOhwO73U6T\njpcF8jCEiKLg+FaWLh7LkJGPBIydpjlx+MfYRQ4VVkRUyhEyCA8Vn6vENOjItPcWY4lqDKKy86lq\nzklkRFlCmV52a5AqazabadmsHa2bdcRqsZEcIVfD6XaB5gUkUmqUOw0D2fvy3mzftoP9+w/Qvn1b\nzu3ejY0b1vHTT6tpf04HPv30Uwry81my7HO+Wf4lKanJuFxOOnTowH9X/kD7Du14f/QHfDQuWDPH\nYjEjUEhOagAi+H7fcOP1fLt8CStWrQwUxoqU4iGEYOjQofz73//G6XRy0UUXsW/fvjrdpz8CNRmU\nZ4GHhRCrhRAf+P+tAR4FBtfQ7v8s9v0STIbTtHI+W2TIkt3b+1bGv/k5Q56ZgMmagMRPVdRd/Pf7\nyivroIy24n+R7r3tMUwm44WwqqlEiSZIXaIoVi661HjQnrz3WUxmw72mCAudL/xzCh5NXDITd0lQ\ntqQ8b39ABmbcC2MZPnQ2vW8YGKDfglFFcdrSufi0Cl+2fyJBwev1kBibQFqbC40XSwhilbaowo7U\nJZboRrz8gBEEb9GpdyD722xLY2ilIP8fiR9WfBGQktF1Nz+v+wYwiAVTRy1kyDMTsCQ0Q2LkYejS\nx75tKwBQBJVcLMGQ/AsPPI/ZZuz+zEo8MaI1UpcIVJqfYxRBeunewViiG/tbqjRs2/NPqQ554Pgh\nSjN/C+wmvI5MPvxyMgBv9n+Z999YwEuDp4D0ULEt9bnz+XCBUYrYiF0Ex7gikbDj+dcEFBTsSgss\nShJS1zCZE3jqPsPff/Flt1Wq9aKQkhSuaBAJLVLPpkPjC7CYooi3p9Kz3c316nNuYW6ATgzgcRk7\no0svuZQ1q9eSltoAS1QsCQnxlJSUsn37Drp26UBxcTHJKSmYzRY2bthMRvpJQJCRkUHLJi247c47\neeyJ/uzetRdVBBcRqiUKuy0Kk2pCMQU/V0w2EmODFOJLL72UTz81FBtWr15NSkoKcXFxHDp0iM6d\nO/PSSy9x3nnnsW/fPmJjYyktrduO7kyi2qWslDIb6CmE6A108n+8TEpZc2rz/2VUWUFILfhQ2u12\nOrVoH/KgGk0qajOYscuGSF1HCCXATe/V6QK6vzGPncf2cjInm68Xj8CpFmL2RZGebQQw2zU/i6mj\nvmTbwZ20btiiTpTRMwFPhGxglzdI/22a2gi7JXzn4PZ6cLqdmNQYYvRoox6MouDxGpPwOwPfJD03\nk7zSQv792cfkFfyEVDREGYFJ9LWHnicn/yFOFGSckYB8XVF1/KrqJ3Vq0T5sHV3RRggFm0hD6oYx\nVVTj3qQlpzDl7flsO7gTu7Dx/sQBlCsnUaSJXL/OmN1uZ/KI2ew4vIeU2MQ6UabPBDzSF6I7BQYD\nsNgdIykAACAASURBVAKJsQlY1XD6qu6vYa4oUUTrjY1At1ADwfHBdzxB4TV3czjrGAt/WMTRg/9B\nU9xEuV3klBTSDuh3zV3ccUlf9mYcxO6zEG2ruwHt3vpKureutoJGzai6E/D/2rlzZ/Ly8rjvvvsC\nY9y+QzvKHeUkJSVxzz33MGfuHG654S7OPudszmrTGiEEv/32Gze+fKOR0W5SmTBhQuAaGl70Svez\neaPmxMcmE22PpXmjUHbn8OHDefjhhwPxmtmzDbf7uHHjWLVqFaqq0rFjR6677joURcFkMtG1a1f+\n+c9//k8E5QGQUq4C/neKdPwPo2GbHpzctwaJsYO4sk+4REXbzr3Yt20pUmoowsx5Fxq1DDqd34cd\nP32BLnwIVM4+N0gjttvt/OPsHjw64wZcahECgU91cWjnIiCUfvtn4okb+vHcjrV4XQYRwRrTNCzb\n+9ZLrueHFV/iLjPUXs1RafS/5l7sdjvzElrhKjpiFJ6yJvPkbUGV2aapjWia2oj8wrWG9DtQJtIZ\nOOKxgGsmLTmlzrXTzxTOveAqtqz+DF36EEKlXddLwo65qs9tLFs0AV13I1Bo3M4gC9x63b189uko\ndN1lyKK0DtXT6tG2KwOG9aNczQQEutDIzw7VYY3Eovoj0alFe6KSzgqoMpgsiQy65dGQY+x2OzGN\nzqY0cxcAqimWR2/xuzabdabw2DYQKqpi54Gbg8SMxNgEesQmMH7iY365e4FTLWD27Lfo9f6SwLl7\ntO3K3r17/4Te+r9XQjL5eSepsCQmq2HIVFWlpMQQkXS5XWTlpjPmQ0P5QTFH0aBBAzZu3Ehm9okA\nO08xWenVswV9bwiy1NJzMnC7C1mzwZDz8ekOWrc7i9WrVwPw2GPhNWYAkpKSWLJkSdjnH38cWani\nxx9/rGfPTx9/XfTm/0GMeOxVFm/owf4j+7nygssjTvAv3PsUy9t0ZOeB37i46z8CzKQnb36YVS3a\nsXn3Vnqc3Z0ru4dPVLruDnFS6lSvaPpnwG638+Gr05nyzSzMFitP3xouOw4w6c2ZfLxoBj6flydu\n6BfYZUwcNo3JX8/G6Szn4evvC9tZHT15HClCVWZ1d3iS55+Jx/s+SMeW7Vm/c2O143RLz2tpltqI\nFZtX075V+4CR7d3tYlITx7Bs3XJaNz2LOy8Lp8Lq3tDkP71K//8KTHh1CtOWzqWktJj+fR+IuAMe\n98JY5nz/BVn5Wdx5xS0BVYL3B49i/qpFnMg8wS29boiYZGoEqIOQes0iqX80YqOiMaU1o7isGIvF\nSlKE/tqsNho1aEZhSREmkzmgm2UxW2jcsDn5xYWoqhqRlaZHKjXsrVdB3P9Z/G1QzjBu6XktRCgy\nVRnXnn8F155/RdjnvbtdXGNmcELTc3FkZBoFuRBE2c98+dz6wm6389zdg2o9rjpjU5VKXBktGzcn\nWm9CmWLsbkyalUsuq58//I9Ar04X1EpR7tG2a8QFRacW7cOEGSuj8wXXsHHdATTFmGCiObPlgk8V\ndclt6nfNXRE/jyQmWRnRtjYUeY0MBUWqpJ11Uf2/4BlGlNVWa/a91WyNWLLbbDLTMLl6HbDkhFSy\nc8uRwnCFKphIqaV41v9f8LdB+ROwfMtKFi2ciOYtw2RL4rknRtZZDqQyPnzufd6ckcTJQ79iT27E\n2Oc/BIxKka+Pew6fIwfFFEXva/vV+hI/OeIx3MUnEIqZThf2ZfAddZP1qCuGTniN/KMGGTCx+bmn\nnBcz48MVDBzxGLq7nEsuu5kHrr4bMOTlf9uwBF33YIlrwph/fVxjkPrLNUtZ8e0naL5yzFGpvDr4\n/TMahziSdZzRHw/F58pHMUVz060D6XvRVfU+z9O3PYZFNbN9838x2eKYOCwooPjU2wNwFR5FEWba\nnHtVrcmNz334AqUZu0EoNGh7ISOfCNfOOh2899lH/P7LD+jSiy2xZUSxx7pg6rtfMPi9Z3EVZdO8\nw/m82s8gXizd9ANfL56C7i3jodtfxelqSZSt+owFh9tJbu5JpNQQQiUpuSGxVTLTTweappGefQKp\nGe65qJgEGiTWjShQGdFRdlKSm1FYlAsIGqY2xmw2CAoVhcEATJZomjZoUsOZ4GRuJh6XUTtFMdnC\n4i71RW1iwbWhLuKQf6OeqJofsPjrT/B5S5DoeF15fDS3+hrqldtGyjN4o//LTH1nfsCYALw78x2j\nAiQamq+M1f+tuX73sGkjcRUfRaKh6y52/Xx6yU+FpUUhuQfLt6wk78hmdOlDlz4Kjm2NWOUPjGz5\nioJelX+ujMmvT2fq258FjAnAbxuXoOlOJBrukuO8PafmuvQrls/F5ytFouNxZvP+aUp6VB2bD2eO\nxuvKRaKj+UpZ+s2sOrWNNMZP3PxPpr79WYgxGTn7A5wFh5BSQ9NdHNxes+rwnO+/oCR9Jzo+I5Hy\nwHq2HdxZx96FI9LYHNz+PZpuCC06Cw7VqF9XW5/HvziOqe/MDxgTgKXfzELzvzf5BekcPXG0xgkv\nNz+7kuijRkFBTrXH1gVVi2Rl5Wf7jQmAxFlWfSkCr8+L5icm+DRf4OcKxEXH0qJJa1o0aRWo7lhU\nVhIwJgA+T3lI4a+q8Hg9fgaacU90n4vswvCCb3WFlJL8/HxstlOn4P+9QzmDmPP9F6xdMRddd4XI\npcsqInF6lYpwYEhyzF/wIZq3BCWQsOdENcVw062Dalztep2hcQWphZ+/MsqrvAhSP3X/7eDRgynP\n2QsoJLXoznvPvMPx7IywinTHKpVsrcArk98g59DPIHUUczy6txTQsSW0YkItWeB6le/sKKu53KnU\nQsfA5woXY6wLNu7dxqzZ7+DzloSMjVblfJHGID03k5HjXsDnzAHFglAsSF8ZQrXSpeet1boFAcpK\nC0PPr3vJyc+rlpSQlZ8VIs0j0TmedWqMuNemjiDrwAZAx5bUmgmvTiEnP894bipdo+p3BBj75RT2\n/PwNUvegmGPQfR6QHkzRDRjx7LgaSRWV7+GKDV+gmKIgcuFFAPKL8kBWnrgVHCXVvwsulyvi5Klp\nGkWlhaBrIARmq504ewwFJYWVDIoBZ1n4FyooykdKH8bNEeBPE1BNVhJqYGCWOsrwVIkR5uQWExsV\nE/F4l8dNeXnou6yaCyiIiZzwWl1/K8Nms9G0adMaj6kJf4lBEUIkAZ8DLYGjwF1SyrCnUQihYdSN\nBzgupbzJ/3krYAGQBGwHHpRS/uVRrfWrvwrkKPjc+Yyd9R4fDZ1AfOO25B8zEuAEghbtguyeI1nH\nWb9rM+t++hrN62eQ+IwJ2aoko/nKWLZsTo0Gpcu5vfh51RF03YMQClHJrSIeV1haxPKtq2jXpjPb\nTu4NyGlY4k7tAZr53QLKcipEATUKjm1l3a7N3PKP69i2bjE+f39Ucxx39gpKv63YvpZjJ0+Q8/sm\nIydHSkp8u7BpqaiqFWfRYcbMn8AL9z5V7bUtsU1xFR1GoqMqUVzW8+qIx+04vIe9x/ZjS2pGea6R\n8CVQ6djlH6fU57lfTAr0S/OVsXTZXPpedBWtO/Rg39agIY1tEHRp5uTn8eOva9mwaYXBiBPg0/Lx\n6uVEiYZI3c1vG7+BGgxK38tvYtbRHWhaGUIoWGKaVjsZL1r7Lc0bNuN3NRpNMyYokyWRa3pcXu/+\npudmknVgfSB3yllwiA8/n8Rzdw/CHNM4UJZaUaxc0+u6QLuNe7dxNOs4ezZ/azxnAsq8v2PWYzCp\nMXgd2YyeM5oPhlS/s4xr5H9vpIbTVc6BwgxuujbclVvxDu058Bs5hzYaNHQJaW17RtRTq8Dq1as5\n99zwypXPj/0XRek7Ar+rphimjV7kl875KFD22hrXgklvGLlmDoeDZVtWsm33ZnIPbQQMEo1TzcWu\nN0EIgUBl+NCZ1bpac/LzeO39J/C5DaUEszWJt/41NeI4r9i+ltLyUr5fPs8oP4BRuXPQwFGc3Tq8\nbkxN/T2T+Kt2KEOBH6WUo4QQQ/2/R5JSdUopu0X4fDQwVkq5QAgxBegPTP7jvm7dIPXQLbLmZ268\n98w7jP50PFmZx2jXrmsgEG3saOag6268ehEq0SiK2ajLUEm7RNdqZnP1aN+Fn1epaNKJipXGTVuH\nHXMk6zijxj2Pz1uEQCG6YUcUoWCPiePVfqeWFFhYEio/LpFk5ufQq9MFDH12LJM+n4TUdR6//YnA\nS/HMqKcoz92PrvvwUoxVSQ7UoahYyQGU1LLjSGzQjJOFe9HxYVLi6RqBTvvu3LEc2vk9UmqYTLGk\ntv4HbmcZXc65gIevu+eU+qyH1So3xviFe59iWmw8e/f9QlqDZrz8oMH737h3GzNnjUTzleHVSlCE\nGVWJ8o9xsL/GirZ6GKV4rXh9uYCKLSE8GOxwOHh+1EA85ScREqxJrYmNjkNVTDxz3zOnlAiZXZQT\nMCYVKPaPzQdDJzNy1ihcLgfXX35zgFDyyuQ3yPndmNjdej5WkQRCIIUewtrzup3UhObN25J3dBOa\ndGIWVjq1CR/jeSu+Ys33s9B1N4piJbnlBXjcDpo1a1MnskgkVP1eFe91BVtvzYYfiI6N55UHjTov\nhaVFATFUTStHColJiUFiKBojpdF/NPJKC6s1KGnJKaiWONwuw0hbbfERjcng0YMpzzaka0xRjUhp\neQFer5s7r7v3T6eVV8VfZVBuBi73/zwbWE1kgxIGYWRGXQHcV6n9cP4HDEp84/bkHd1gTHLCTvfu\nlwX+9tL94eICG9Z9HajzLqQVnyjFQhJmYv0PobGjSWvZKaxtZcz6ahrgw+wXMzz86xqocr2J8yfg\n8xrbY4mOI+cg098/vdjJXVfcypvbVwRKF1uimnGDn73WqmHzsNK6q3aspzzX0LZSFBPoMjAR2HwJ\nKMLwJatqNHddfUeN1875fTOKakcBdN3BpM8nhV3vyG9rAj51n68UR2kBHw2dcFp9btWhB/u3LUGT\nLlQsNGgVLFz6eN8HoQob6rPFn6D5jPo2KjY8ohiVKEzYUTQzFaXe7SmRd5UV+OCzj9C0koBgZWnG\nrwEJlApMWvJJYMcgBbgLDvPI3WNOKz+pR9uuWKIb4Sw9DEjMpmTuv8pgc9ntdt4ZNCKsTe6hLZUE\nQ1V8shyTiMGix6NgJEEqwkzvi2tWdNi1+XsUxYyCGdD55rv5YezI9WuWBN4hXXdTnHOcKW/NOeX+\nAvS++HqWLjqAJssQmIhJC07St/S8NizXasyn4wNiqIqw4RJ5hsClsGHzJSJUI1RtiW5U44Q/b8VX\neMrTMamGi8tTeoL5qxaFEGw27t0WMCYAPmcmDdIuOmXjeabxVxmUBlLKTAApZaYQojqOnU0IsRXD\nczpKSrkYSAaKZHBJlw7UTIX4k6CqJowAmfEymU3mGo9HD+5CTGoUqqkBCU3a0/6sczCbzOzav4NW\nLdvXSK0FwrK1w7K5ISwoWLXNqcCimAPFU4UUyCpigVXhrhJLsohkohLbEJuUyj1X38N/Vi/G43Hx\nQN8HaqTWGgj9/roeIV+jqnLBaTJYoGJMjVWnFBKTueZXSFb6XopqIUo0IblFN1JTGtGpTUdWbfqR\npKS0iAuOygjPXZC4NQ92ggZFq3IPpACn+/RzOnT/9YydisBsqeW5rnSfzUoCpujGJKQ25aqLr2X7\nvl/Iz8+m90V9IlLnqzuP8Wv4Mxv+2ek/1xWMK4lEIFFq0carPMZCUbHpaSS2vIDomFhu7Hk9X638\nCovFxtD7a85Wd0cYK68ndEfscrtCYmMQTh74K1FrTflTPrEQK4CGEf70KjBbSplQ6dhCKWVi1QOF\nEI2llCeFEK2BlUAfoATYKKVs4z+mGfBtdTXuhRCPA48DNGjQoMeCBQtOq19lZWXExEQOkh3LOBQy\nUQvVWiONL6cwD2d5MHRkj00iNb52ee6qKHGUUliQTcCQ2WJonBK6rXZ6XOTknEDHh5AC1RJDswZ1\ni51U1+fswlxcVYKC8QkNSIipvgzr8cxjATeRUEw0bdgCRak/2TA9JwOtghEjFNJSmxBVReYlI+ck\nXk+JoVyASlJSY2LtkceuMmoa4+Mnj4a4NoVQad4k3MVYgbqMTV3g8XnIyj4RmEAVk41mDUNzVDRd\nIz3zqBGzkEYJ3eaNW9bp/NX12aDihhIqzLZYGqdEerUNpOeko3n8biOh0CC1KTY/k6k+yCrIwe2o\ncH0KYuNTwpIMc4vzKS/JQwrdcOXGptT5HaquzyeyTqD7KgfzBS2atqn2PGFjY7bRrMGp5Q9Vfr6E\nYo44fsdPHkXTnCAM7b6mjVqiKjUv5qDm57o29O7du0415f+wHYqUslohHSFEthCikX930giIyO+T\nUp70//+wEGI1cC6wEEgQQpj8u5SmwMkavsc0YBrAeeedJy+//PJT7JGB1atXU905nhg6MeBWAohO\naU+/e2uuib1qx3q27NlGzy4XnVY9j13H9rN8ww+0adEmbEsORgGordu/QJcuQMUW3ZTJd8+r07mr\n6/OMZZ+yYd0Xgd+FhPv7DefyWsq2Tv56Nh6Pi8euv/u0BA7nr1pERlYGt/e+KWK9+EFv9sdVfBgp\nfSjCQrseN/HS9bXrmtY0xoOGP4K7NFgEyhLVgH7311x+ubaxqSty8vOYt+ILUhJSeDBCEuG6XZtZ\nu+wrNN0vBW9JZvSNM+uk7VZdn49kHec/748NYe2173Ej911ecwxq/qpFZOZkcutlfSOOTV2xfMtK\ndv2+h97nXRLRdffchy9QlL4dKb0IYSahaXc+fG5Mnc5dXZ+HjHmeksxgKShVjeahBxbXeK703Ey+\nWLmo2rGpDz5eNANFUXjy5n+G/a2wtIjPvhuP12PUvFeVGP758PA6zR01PddnCn+Vy+tr4CFglP//\nYQI1QohEwCGldAshUoCLgfeklFIIsQq4A4PpFbH9X4EevW5iy5rP0XU3JnMCj9/3dK1tasuOrytq\ny8Det/8X/4rGWMV7nadXSwOg/w33s+PXDThzD4AQxDfrWqe+1OrCqyNqS970luUghAkhjMf8xNHT\n14O685b+zJ8/Bs1XhqpEcfU199XapraxqSvSklNq9JWv3LoKiQ9FMXYDmq+M9bu3nFKCZQVaNWxO\nSusLyTu8GYmONa5FnapF1jY2dUV1qhIVcORn+EUnVf/v1a4t64znH3qBt8YOwefORxFmOvSIzCCs\njKapjc5YHKMm+vj63VvQfGWBMZZ4WbV1zWkXlztT+KsMyijgC39tlePAnQBCiPOAAVLKR4GzgalC\nCB0jAXOUlLKCo/oSsEAIMRL4BZjxZ3cgEh7v+yAPXHE7B7KOhgXflm76gd2H9nBp91784+zwWto1\nYePebfy0fR1d23Wu3e9cBRVU4aioWMoqfS7UKN777CMaJjcMkcxIz81kwYqFJMYl0v+G+2s9/8cv\nT+RI1nHMqjmMvTL+q6noUobod9UVM5Z9SmFJIf2uvqfeApAVVGFhjgZ30HWhqGZGfzqea/5xVcj4\n1GdsKhYAOw7voV3DliH9qhinc87qWO9J/MDxQyxet4zGaY154MqaCQmRsGjtt0RbohGIwG5CYGbn\nvh0cyTweMkk5HA6mf2vUvX/s+gdrHZtRT75FTn4eReXFYQoPFeN0z5W311t54Ms1Szmcfogbel1b\nb4N7JOs4m/ZuQ1iiwRVcHAmThdGfjg/TWavP2DRNbcTUdxaw69h+miQ1CNnhHck6zpcrF5/SOBWW\nFjHz28+w2qIilgCvDSu2r6WwpAhFWEIqaDqc5YyZPyFMZ+10xuZU8YfFUP4Xcd5558mtW7ee1jlO\nZdv47tyxHNrxPRINRVi47NqH6/wwzvn+C9b+MAtdehGotO1+Xa0B3AocOH6IDya9iM9bgkBFtaUg\nfQ5QTOjecqT0ICTENu7C2Bc+8FOLhwTyLKJT2/PR0Amn1OeBr/fDU25UpLTYGzL5rbl1bvv0u0/i\nyDPK7JrM8Qx7/uM6vxAjZo3m+G9GlUuh2FHNUUjNg1BMaJ6igBL0zbcPpu9FVzFy9gcc/fWHkLFp\nakqpd3/nr1rEymX/RpceBAotOl/JsDrWaNl2cCdTp7+BppUjJCS26BHGWKsODoeD5999Ao8jCyFB\ntaUidTcSBaF70DQjzmSNbcak4Z8AMGBYP7wOY2zM9kZMeWvOKY3x4NFPU5Zj5Pao5jhefnZsnd1b\nlanFimLjbn9p4Lpg5ncL2PDjXONeCwuqNR58LqRiRncX+7XuVM656BaG3DnAoBYvn+kfG5WWXa7i\ntYeer3efdxzew6QprwTGKa5p1zq71wpLixj67gB8bsNVZUtoxcRh0+p87WfefYryPIMhqZoTEEL4\nGYwCzWvEmUyWRN56YRJpySkBej6AyRzH0GfHcmzf4VN2eQkh6hRD+Vt65U/A0T0bA2V8delhw9pl\ngDEZDB79NANevZ/B7z8bIl9SgU0bvgskUkk0Du9aX+frTv5iYsA4SDR0TzHTRv+H+IatqcgDlQJK\n/TW/py+aETgewJF74JTKzM75/ouAMQHwOLKYttQwKIvWfsvA1x9i4LAHmbhkZljb9NxMw4Xmh89b\nzJT/RK4kGAnpe9YHa7vrDmyJDZk2+j+gB+t66Lqb5T8uBODE3k2hY7Pu23r21sDan5YGVo0SnfR9\nmwN/e+njVxnw2v089dYTHMk6HtZ23pLZgQREo4Tzb2HHVIfxC6ficWQF2mquXF4Y9B5nn9s7YEwA\n3KUnWLrpB2Z+tyBgTMAomDVj2af16yzGBFmeEyxvrHlLmP4fI8lv28GdDHqzPwNeu583ZrwbsX3e\nkV8qjYeLJd/XnSzz8/pvgvdaelBMFqaO/g+KyRrIc5Fo7N9hSP9vWP9dpbHROLF3Uz17a2Dukpkh\n41SaESwtPHL2BwwY9gCD3niYdbs2h7WdsnhmwJgAuIqORDwuElZsXxswJgCat4guF13HoMffChgT\nAJ+nkCnfzCAnPw9HyDtUwvRFf44T52/plT8DItRuezUPr015i/ycY3iKjxmfZeXw8sRXGP+vcaFt\nqzKgRO1rgJz8PJZvXYnX5TDordILqEgk81Z8hcsVKkdRUTtQVavSQQVmS/0fkagIhZD2H9zFG9Pf\n5uSBTQE1gR0/fcGKZm1CVqYWxewv+xrcOau10a8xXBoHjx9C6rqxcpMaQpjxuZzM+f4LtCo78Qqf\ne9j9rMP9jQRRhWUjdZ3XprxFaUkBZdlGnRCvM4f3Jr/O5DdnhRyrqFUYOqIKLzQCHA4HS37+noL8\nHJDSqM+CAMXE8m2rKCwOXQgICTaLDZstnG1ltdafgWUU1VKAIGW2pCiP16ePJPP3neg+g5ySsWcV\n479KiiA+Gnqf68L023VsP9v37UTXfAajSvoQwoTu9TDn+y9CaPhAYCzFGRpjVQl9F3Tg9ekjcbm9\nFBzZGDCQc+aNpteohSHHVk0hEBKi6nDfF29Yzr5DhjGR/kWRopg5mXmM9X5FgMo0YrNq8b+zVd6h\nsHf7j8HfO5Q/Ad0uug5FGAldUvdS5viFg79/Sm75KoP+54e7KFzY7abr70f1a3spwkK3i64LO6Yy\ndhzew2vvPc6a/07HkbcfJ9mUq9k41JN4tQJWfTcVZ/aeQPlcgUJaG0Mu/Ll7n8JsS/N/Lkhq0eOU\nqj/eeVlfopKCvnafVkJ6xhL2HphNiQyunHR8bPotdJWWlpxCYvPuASNnjkrjpXtrdvENmzaSGf9+\nhTX/nY7HV4hDOUm5mo2TLFz5B1jz3+lIzRF4v1RTDPfdajCzulx0baAUrapGcduNp0YYuPWGB1EV\nw5BKXaPUd4CDv39KRt7XeLXgKlJzFoS1ferepwMlnAUqjdrVLAtTWFrEc28/xoqlk8g7thm3zMeh\nZlGuZuLVitm57nNO7l8L0j/GEuxp7bmy+yXc2/tWohKCNOeohNanFLOx2+0kt+wRGCddc5Kbv5L9\n++dRJLcHcjMkkqNH94e1b9X5ssAzqJpiefjOgTVeb/LXsxn/8RBjjJ0ncYhM47kWmXgc6az573R8\nrmyEbkxpimKl5yVGqYNb+/ZDVSveITNdLjo1pt0z9z2DyeLPbtAlbj2L/fvnceT4PFx68N3VvCVh\nO/sX7n0KS4yRLickRDc6p9aE06feHsA3Cz/g4I6leLRCytVMHGoWbj2fzAPr2P7TfBBqIL/KEt2I\nwbc/QWJsAkktgmNjtqXxXA0yRmcSf+9Q/gQMvOkhDnTrxbZDv/LdkvfQ1YpUMR2HKZdYafidrXHh\nwedrz7+CTi3PZu2uTfQ4q0utsvczv5yK5jNqSftwoKNj11LxSRcetRgT0UghEUKl783P0LxhE6b8\n+y3uff4cVN1Kq7NvpfvZ99MwpUG9yQOVMeHVKSzfspI1W1Zz+LCfWiwFHms5vvIyTGoMQqic1zFc\nW+j9waPYuHcbWXnZXNPj8lqDxtkHfw7Ig3jUYky+GMwiCqcoxCvKMRNPhbDh5ZfciIZk8tRBjFfK\nsMuGDB0yjW2HfqVPl0tIS04JVM6rD67sfgldWpzNml2b+G7Jx+hKMFfFYcsl3mvUBldt4Qa6VcPm\njHp1Oiu2r6Nts9a1ymeM/2JKoEqmLt14TA7svlR0vHhMxZi0GISiItA495L7aN+8DQu/ns19zxmp\nWompl3PnPS8D1JvkURmjn347ME6fL3ohIBSpmby4vYXYSEFIaNYsPIfj5QeHsOvS69l79CBXdu9V\n68Jlx6ag69enlKLqVizE4BYleEUZVqxIJCZ7KldefS9Noxswbd6/+Pb7N7Hpcdx+x1u4NBddW519\nSqUjwAjWjx02i2VbVvLDd3Pw+oz8HCEVHPY8rC7j/VVNsRGJJJPfnMXiDcuJt8fWyoZctPbbQJVM\nXffhMZVh9SUiUHCa8jD5YlBVKxKNRu0u4fwuF7L514089mpPdOEj1taZ/o++W+d36Ezhb4PyJ6Fd\n87No1/wsvl8SFMNTNQsWXwwmezLWuBTefTqyv7lpaqM60zBlZbVVIRFCoAgbShWdMYHk1kuuZZz9\nPgAAIABJREFUZ+DwR8kzbUKajVXO/oNzeOvxYfXsXWRce/4VbNsbFNkTCMyeKExKPGZbEu27XVrt\nhFYfYxZa81yiCBVFsRnzW6XEaUU1cedlfXlwSHdKYwy5GBdFjB73FDM+/K4ePYuMtOQU7rysL99/\nHZR3UXUzis+EyZaMao3h2Ydfjtg2MTYhYgXHSKisCiD88jyqYjUS+CsdJwV0bXMOm3ZuJtvzX3S7\n0c5d8jUFhZdz39W317+TVVAxTp8vDhIQTB4bFhmL2ZpCcvMO1dJp60WnruSylBg7a0WxocgyKvda\nKII7L+vLI0OuoDDmIGCM8ZdfvcqccT/Xr3MRYLfbufOyvqz4Phh3ElLF5InCbEtBqBZur1IiuTLq\nmofk8lXOmjf6pwqzQYNHICpp/cXFJnBLz2uZt3gQvmijnUuuZcE3DRn/4th69O708bfL609GQuML\nUfyuCJNuoXm7G5n6zgI+GjrhjKwi7urbL+AiMxFLlG64sEwiGrs0BAWFhMRmxnbbU3w8RIjSa3Gw\nY/9uzhRe7fcc0XowIz/a14jRr8xjytufMeTOAWfkGknNuwa291F6GmZhZAPb9TRMIhYARbFx23VG\nzojLHCo86ZHZZ+R7VKBtj+tRdcPFKaRCYmJPpr6zgElv/PuUV8eV8dRtTwRcL4qwEa01BqGg+n+u\niOfYElrTq9MFHNy3BV0JGiFN9bF+c+T6NKeKOFOHwBhYfXE80G8kU96Zz9sD3jgj52/b5ZJA3Muq\nJ2HF2NFYZSIWKu6Fia7nG5Rglwh1OblM4dL6p4O77hiIxWc8ZwJBgtKZKW/PZ/KI2XVmq9WEe3vf\nijXGeG8UxUyM1hhFWBBCNcbYr3tntqXw+E0PMXnxJ/gsQSMkhaQ85+Bpf4/64u8dyp+McS+OZeqS\nWezet40Lzu0VUjTqTKB76058GpWAr7QU1RRN91534HQ5aNW4Jd3admLRmqU0a9g8sBq2xDdHePcE\njIrZY6db+3PO6Hf65MMV/Gv8K2ial1f6vURKSv3ySmpDhzad2XBsB7rmwBrXist730xGdgZ9/3EN\nB04e5sCxg1x9UZ/AatjmjcdrCRITLCJcufd08NJ9z7CweWt+2rCC1q3aM7iW+EB9kZacgjm+Ad6c\nPBRhpnW364m2RxMXHc/dl93E9G/nEhMVE8gjatvhfHJ3rwoYFVUzcfEFp57sGAnTRi/kzRnvUpif\nwwN9H+a8jl3O6Pkv6nwBB3asxOcpxBSVxhVX3cfJ3Az+0eVCNE1j02+buahzsDSzTabgIMiqsvnq\nHwusCX26X0q87RPmLZ1JYnIab/SPvPM8HSQ1bkXm/nRAJ7HVP2jXugNur4f+19zL/FWLKHOWBfKI\nBt7yCD9uGBkwKkIKolKrl4v5o/C3QTlN5OTnMXL6cLzOMlKbtmXEY6+GHeNwOHhl0qu4SwuJT23K\nqKdGQgRZhTOBN6aPwFOeiVBM6LqbHRuXMnXUV4G/V3U/TB7+bx596TbKvPsxSTPtO9Uu6z7zuwVs\n2fQ9KCq33fhQxBXZ4g3L+e8PX4KUXNXnDt4ffGolgOuCTau/MHIMFBOeshPs3LMtoILbrvlZUCWR\n7f67PuCzz1/C44+hzBhbu7vrX+OHUlaQRXRCGmOGvBfxmFcmvU5h9nEsMQkMHzCc23vVzYVVX4z9\ncgrO3H0IoSLROfLrD4wfsTCww63KqHrh/qcYOHwHRSU/IzBiKLW5u1btWM+XS2aCrtHjgisjJrnu\nOPz/tXfm4U1VWx9+d5KmcykUWhAEBEFEUQRkEq6ieJ34VBQVr6JwUUaRQYQygwwFRRAZKigiKiLo\nFeQiTihXQAQUKIOMMhVKoQOd0zTD2d8fJ02bJm3TNrRQz/s8PPSc7CR7Zydnnb3XWr91mA9WLUCx\nWbipVccrclHNZ9XaRdhtWY58osts/+1bl5LDRTPFp478iCnz+5ArkglQavBSv9JVpqcsjyEp/hh+\ngSFMfHmqRz/InFULOHt8HzpjAK/2eZ13rtCW0vZDu7l4/FeHX0pH+tnfiWzX1bll5mk+bmz0HOf+\n2oCisxFqvIWFY99xa3Ol0QxKBZn67uvkZavOuQtHEpj+kdEtoW3sO69hSv0LgOTsBKIXT2L20OlX\npD/mnCyX46KVCj3xwZyvvH79LXG/suOnlSgOsee1q+fStlkrF6fq8fiTbFq3GLsjPHjThiU0a9jU\nJ/IjnlDsrvUrcrJKrqXyYKduPNjJuxwAcNV2smQnMGLuSN4Z7XohGR87hUuOwkqW7AQmvRtd7hrr\npZGckuhyrEgbp5LPlfj5xk79wOvXT8tKZ/VnbzlzLnZu+ZQm9Ru7OZJjl01xatcd/G0dn4ZHlCti\nzBsUi+sc20utpdKQFfN/8fr1Z6x8m4Q/f0YKsGSrv+sl01zzpGI3rOTE3m+cPrt5Syfx3szPvH6P\nsnDi3CmXOjQSydkLZ0t8jnpNuTLXFW/RfCgVxGoq2KuVAi6d+8utjSXTVfsyI+mcWxtf0eq2js5w\nTABjuG+V/bfH7XAaEwC7YuKXg66JYj/FbXcaE1ATCbfs3e7TfhTGL7RgjELoubNtxfewC5Ob5noB\nN1++6NYmvUjCYtE59yUPd33EqeUE4BdQx6fGetfRvU5jAqrB2hbnOn9JqSkuQqgSOwcO7/VZH4oS\nVq/A9yQk1PNxIamL50+45HMU/l3nc/z4fpcAEJvZPQTcVzzSvjt6vwLVbr0+mEe7lJwycDWgGZQK\novd3lWoPi3Dfj9cH1HA59g91U+r3GQN69KHTvX0IiWxJ3WZdfH6X3Kp5K0Shr41OGGlXJJ6+/c2t\n0RVa/Ar03NHMY3UBnzD39XeJatqJkMiW3PPgSz6/S/YLdp0/Q5FjgIAikumGAPc2vqLLre158ulR\nhNS9hVoN2/LGqHd9+vptmtzmYrAEOlo1dZ2/yIjazrwbUC/yjRo182k/CvPW8Nk0bvVPQiJb0rJD\nTyb39aoen9eE1XL93eqN7iUY6tZr5NrGL9infShMZERtXh08i7B6txFWrxVDBs2qkGpzZaFteVWQ\nl18cwwefzEOx5BBQq77HCnYj/j2Bdz6cid2cgTEskllDZvq0D0mpKUyZPwJrbhI6fRD/+OfzLBi7\nwKfvkc/jnR/k4PGDqrSITk/bzo+46Wy1bXY7t3Z6nMN7fgYkN7W+x+dqqAvXLefAjvVIxYIxtD5z\nx/gmSs4TkwZOZ9qS8dhyUjEERzB50Ay3NjMGvcHYd17HknkJvX8oI/pP9Hk/Xpk5CPPlMwidgRtb\n3++uquAjIiNq0/GeZ9n9639BsVO/RTt6dnWvrvjQowP5/rtPkXYrEY1alkvwsCQ27vyRDetiUWzZ\nGAIiGDVwhk+i5Dwxa/A0hr81AnPqeXTGYF7qM8qtzdjnhjMm5SIZiScQfgE8/mjJZQvKw5gF40iL\nV0Ptaza6g/mj3/b5e1xJNHHIMlIZNQXKyoi5I8lKPOQ8NviFs3T2Fz57/atxzANefxS7UrCPHnVj\nZ2YNnuaT174axztj5ducPvCd81gn/Hh/bvl0xzxxNY550ITnnAmcAEERN7JwvO8qfV9tY96480fW\nf/FWIbVoQa/e0RVKPi1MRcbrrTiktkKpBthyXUUlpVK2sq/RiyeRdu4owmDkwYf7VKgIVGWRL/aX\nT26RYISSOHT2GEs+nI09Lwv/8LrMfvXNSsskLi/ZWa55FFKxkpSa4rW0/9zVizhxYCsguLX9P0us\nuXG1IO2umnP2PHMxLd1Jy0pn0rvRWLNS0AfW4PWBU676LaNzSRdcfDQSSfylBK+fv3rLOn7ZvBZp\ntxHZ5HamD/D9Krk0NB/KVUJaVjrDZg1l4LineWXmoDKp/N52R5cCsUMgsPYNXj93wZdLSTm5E5s1\nHWtuEpu+XlqmfleETzd/yaCJzzFwfG9iPilb+KV/jYKLg074cXfn0osg5bP4wxjyss9js2aQk3yM\nyUunlum9K8Lo+WMYOO4ZhkzpR9ypw6U/wUGPex51dcSH1vfamGyJ+5WjezZis6Rhs1zmwK9flum9\nK8L2Q7sZPKUvA8c9w9iF7iH1JVGjXnPn3wLBjS3v9Pq5U2OnkHv5JDZrBnmZ8by11H0r+koxdfls\nBo7vzeCJz7N+x3elP8HBk3c9gqGQI97gV4PHO3nniDeZTPy86UOsZjVYIvHYVlZ8W7Fy5+VBW6GU\ng7hTh1m28i3s1lzCohp7XbuiJKYsnogpVRVOtF1OY/rSSSwcH4vJZGLcwnGY0y9iCApn3KCpbj6L\nAT36EBQQyN4DO6lZK8rrOhwA5xNOu0S3KNZs0rLS3bSV5qxawOk/d4Jez33de3stE1IcSakpbP1+\npTMa7GTcd6xrfBM9uz7M+h3f8f23q1DsVuo3v8OjA3bxpGVMWjaDnOx07u38QJkKWinmTJdjU4a7\n8U5KTeGN9yZizU7DGFabN16ZWS6hzMJMWjaDtPP7ALBZLvPehzN5b4Yq4RG9eBJpCSfQ+wXQp/cw\nN+mZLre2x/rcODZuWY9/QBATX/TeKR134qCLJI8ibew7dsBNM+zj79eyY9vXIBWa39bVJxUIP1n1\nNjaLGg2VcmY3MZ/MV3W8zh5jyYo52C0mQus09Jjb8+bwGN787F0uXoznlpvu8KrgWz55RVZ09lzP\noeSj5o0mJ+U8ev9gRvQbX2EfzYpvP+f84Z+dK41N62KdK/43P3uXU4d+A72eu+990k1OKTKiNtEj\n5hO7JhaEYPDTg7y+aTh+8YxTxRvU1c0JH1QoLSuaQSkH7y2f6dzbvRyfypTlMUyrYFKXpdAPQEpJ\nTvIRBkb3QrGZsdvNCJ3AZrlMTOxkFk92rw/yfPdeHqObRs0bTU7SGXR+gTz3zDA353irFm3YGr/P\nGfOu96/pduFMy87gxN5NzjY/bFzqlaBfSew9dcAltNiumPnm68V8980KrJbLTsnx+IM/seLbRvR7\nyD3h0tOSfvuh3Xy6ZhHSaiKodkPmj57n1sYQUhtbesEWWa16jd1fe+kkpzifLeUyU5dMqXCgQ1ry\neZdjc3a8OseKDbstAyEM2PLgo4/fpFPMGrfnF1cuesryGC6e2IvQGeh8d0+XCpwA3dr+g8O7CmqI\n6HQB3HNHZ5c2FpuFbZs/RnFslx7+fQObm7WqsIyIYikw3lLa+Gvftwz881ds1izyhdbSzqcyadkM\nj/Ppqdzw+eREZi2ZhD03Hb/gCKaPmOP2XQyrXZ/U+IJwb2ONSLfXSUi+QEbCfgBseanM/2AasW98\nXK5x5nPi9GGXbSuLLYWXxz6BAGzWNGc56i2bPnSKkRbmhroNeXO4u6Zf7IaVxO38FqRC45adGNdn\npMvjrZu0RG8IdQrDCqHnzls7VGgs5UHb8ioHdovr3c7lS+eLaek9ATULvvBWJQOTLolM61EyxHEs\nFMS720zeaxJNWR5DRsJ+bNYMLKaLfLLKPWLkhQeepmX7x/APqU9geBMGvzzVrU2uOdclyUpRzMSd\nrJjeV4cWbQrk3qUkV5dEjnKWTOthsgxnUJT8gkiSE6e935755LN5WE2J2KwZZCYeZPL77hF1M0a8\nSUhUS4wh9Ym6sTMzBk52a2PJdv2c87JT3dqUlaj6BXe/dsVEriFZnWPlCGYKVkmFL8Kl8enmL0k4\nvEXdssxLYfvmT0jLSndp07pJS+595CX8QxrgH9aQR3oOdfMn5JhzncYEVJHRPUcqnleiDygIkTeT\nQo5IINN6lEz9SexKge+vqLEtiZj3ppKXeRabNYPc9FNMWexuiN4cHkPtxu0xhtQnrF4r5gx/y62N\n3eYqmFrcKqYstLr5DmcemKJYyNWnkm07Tob9CDm6RKfUvF0xs/fUAa9e83j8SfZuXYMtLxWbJY2T\ncd/z3e8/u7Xr13cCAeE34B/SgNvvespjZN6VpkpWKEKIWsAaoDFwBnhaSplWpE03oPDGegugt5Ry\nvRDiI+BuIP8b0FdKGUclYQio6aySBxBVv0kJrUsmLSudPScO8nqfMcz9ZC7m9EvYsjNRhB2BgqKz\nk2fIwl9R8xz8Qr3XwUpLdnXoKVbPjutRzwxhS9zt1K9V1+OSPzgwWJX5cGyb6HVBdGjuLjvvLXGn\nDpObl8sTTw9nw8ZPsFpyUaznnAqqdoMFm8WEEaOaA3Gz9++lWLNdjtNSLri1qRkazoT+kzhw9gid\nW3hWNfYPq40tucCIBHq4w/UWk8nEjqN76PfI88SaTVxO+Atz1lkkEkXYkDqFPEMmgda6gGeJ++I4\nduqoyx2xXTHz59njbivRZ7v1pHkD9XvqqQ5HSEAQOl2Ac9tEJwy0v6XUoJ5iOX0xnjMXz/HKgCnE\nfjwPxWLClJOIFHYkYNdbsVizCES9qYis773fz2ZyNZiWnHSP7aL7vs6eEwe5rdHNHoMuDH5G1+Og\nWl73wRPbD+2mbdPbSGz/KCf+3Ik1Owny51go2Iy5kGMHvQG9PpAOLdp49bp7Th5wSveDmkS6//hB\nt+ivTje3JfSF10jPyvB5mL63VNWWVzTwk5RythAi2nHssikspdwCtAanAfoL+KFQk9ellF9SBYx8\neSrvrnwTmyWHiPrNmPjia+V6nS9+2cgPG5eiKGb0ukAe7jmExzs/yEuvPYzZYSv1dj+MtjD8QqMw\nBoczbah7DkRxNL6hJYeTjjl9JIbACLc2aVnpjJszFGtuEgIdUc06uynE1ggOpVXHnhzdvx2hM/Bo\njz7ljooaOXcUWRcOIgUE1GjMkmkr+HX/Hyz+uB92of5o/PKCMeprYwyqTdOWHbyW7lfHWMtZflhI\nuN5DBnnshpXs3arqf601hNKv7wQ3n8XsV99k3KJx5GWlElgjkplDvP/cCxN36jBLlk3Cbs1EJ/xo\n2eFRZg6awsDxz5FrSUICOkWP0RaOMSgKnX8Qg1/w/vvU4bYOrD+23VnG2GAIpU2TW93avTJzELmX\nT6rFnaJasGDsQpfH/Qx+PPzoYH7cvBZFUWjT4f5Sa3YUx5TlMSQc/gWJHT//2rzx2kIiI2rTZ+Sd\nWFBvavysAQRQG2NgFDXrNy1TomJAeF2ykwpW7SG1rnNrs3nvNtZ+/jZ2ew5rdP507f6C21bgdbXr\nUeeGDqRfOoMhIIToAeVTRjaZTLw2awCW3EvOgnWxb3zMiLmjyU48q9YfkjqM5hD0AZH4+Qfx8EP/\n8nrLuOutHdmyaYUzTF4njPyjTRe3dmMXTiD1zO9IJKuCr+Pt6NhKj16sKoPyGHCP4++VwP8oYlCK\n0Av4VkrpXnS9CmjesCmLJpUeDTX5/ZmkpybS9MZWHkqgws+b1zjvCO1KLt9//zmPd36QyKbtsB5P\nwy7y0Es/DH71sJszsPoFlslvMeqZIUzPzeHC6T8xBAQzqp/7Rzx31QKsuao/SKJw6cRvHl9reK+B\n4GEMhdlzYj8fr1+BEIKXnhrkJgey/dBupzEBMGecYeG65Qzr2Z+PdDditp1DouAnwxBGf2zmTIQX\n5XALM2HYHOZ8MAObOYeohi087sHH/faN059gt2Wx6qv36TTB1aAEBQWxYEzpPpO5qxcRf/Yoteo0\nYGr/aLfHP1yzBLtV3cJSpJUje36EpwZxW4fu/LElHqvIQYceP10UtrxMdFKhbrC74S+OHh3v50LK\nReJ+/xmhN/DsEy+7XURWfPu50x8kBeRcOsrGnT+6BTL07PpwqdskSakpzPl4DjaLhfvv7uExGOLC\nkQIDZ81L4e3P5jNn2EyCQ29GZB1BwY6fDEZvrIEtLwO71e72GiURMyyGie9NJjcjhdBa9Tz6HL76\n70dO+RhFyWPH1vVuBgVQhVpL4ePv17InbhuBwTWY/O/xbp9vzKfzsOSqJRAkCkknd2EymXise08+\nWbkXC+kIdBipDUoeNrON8BDv1TIa1KnHE0+PYMOmVaAodO76iNsNUFJqitOYAFhyLvD22sVlCtDx\nBVVlUKKklIkAUspEIURp+wm9gaLe1ZlCiMnAT0C0lLJsyRdXmOFvjSD7oupnOJh8nDl5ZsY+51rK\nVipFfkh2VSNr1pA3mLI8kJQLp8nLSiZbcWxrZJ5g4LinWRqz1ut+lPaFslktRc4omEymMt/ZnL4Y\nz9L3pzh/xO8uGceMMctcnI7ZudkuEWUAublq+9eHzWXRZ/NR7DYyk/dhUtQtxT27T7E0MJiBXqoz\nN6hTj4XjFpfYxqUIGaDYbMW0LJk3PppD/IHN6kU6+RivzU/l7ZGue/WKUuS1HXM+9LF+zLfk8dfR\nfdhys8nI26fWSc+DsbN6s3zeZq/7MaBHH+jRp9jHTUWEFaWA9Ozy+Qsmz3sVq1ktd/v1l38RHBDk\ntpIp+vnabOp3LOb1d5n+/gzycrLIvXyCTEWNQso6e5LR71iZO2KOV30ICgpi3qi5JbYp+tty+615\nycffr2Xrjx8ipZ1sYPRbw1gyZblLG6u1yKVHKuTZLXRrfRcpGaP4ZftGrJY8sjL3YHf4Bt//YCgt\nJn7jdSmHB++8t8QExxyrqUihObBYKv+SeMUy5YUQm4G6Hh6aAKyUUoYXapsmpfRosoUQ9YADwHVS\nqhuJjnMXASOwDDgppfQYaC6EGAAMAIiKimr7+ecVi83Ozs4mJCSk1HZnE06CLHBkC72RhkW0gC5d\nTsJsKvhhBwbXJLKm6xfs7Pnj2CnYP9VJPY2vb1He7rthtuRxKfm8s686QwDX173epY03Y05KSyE3\nx9WRHRJWm4gw12mNv3AG6ageKYSe+vUaodcV5NBkm00kpZx2eY5BF0TD67zfYy+NCymJWM35vhZB\naI0IahXSV/N2jguPBdTxNCziT0vPziQjXd1LBzD4B1O/jusWzdmEk9hlQcSbAG5o4LuaNIqicD7x\nLNIh6il0BrfP05sx51nyuJjkKoLpaTznLp1DsTrGIwR1atcnyD/Qpc3p867RUHr8adTAd/U7kjNS\nMWUVbIsZA8OoV0Rnz5sxn7t4DsXmmlDZqIGrZpn7b8if64sEPcQnnsVmd/XvhYREERnuu9pA8Ynx\nBeriQke9qOsxGgr8RN5+rz3RrVu3qs2Ul1J2L+4xIcQlIUQ9x+qkHlCSNOvTwLp8Y+J47fx4wDwh\nxApgdAn9WIZqdGjXrp2sqNSCt/IFgya87yIbERLZgheefdGt3fod3xF3JI52t7T1uH3w0ui3yOKk\n8zjIHkXfPt7LcnvD7E8XcPLQL+gMQbw2YJqbY96bMW/c+SM/fLGskGyEjr79pnt0Ds5dvQizJY+h\nT/R328JLSUnh1VljsReqyV438kFe+JfvdKJMJhOj3xqOLSeFkHrNmfe8a8lWb+d42KzBzrIEAMbg\nerzwnLu+0/ZDu9m862eaNGjCcx62XQZN+pC03AI5fT97MJ8+v6cMIyqdz378ki2bP0cIHY//30vc\nc9c9Lo97M2aTycTwyb1cHMS3dnyS5+75l1vb2A0rSb58kSe6Pe5RCfnDEWPIMxREs4X73c6LReah\nogx/cyS5KWcw1ojizZHz3Fbd3ox5/JLJzrIEoPqoXnz+Zbd2x+NPsubnrwivUYthPV9we3zcokmc\nPvOl8/ehk3qG9ltJl1vLH/hQlLhTh4n9YDrSnsct7R/ixe6uyb6VITVTVWHDG4D8q+uLwNcltH0W\nWF34hMMIIdQN9seBQx6ed0VZ/s0qpiyP4bcjnn/4vZ4chMFYE50wYgyux4SXPDv8Hu/8IFP7Rxeb\nmNe37zSC7JHoFD8CbOF06V6yH6OsfLr5S/7a9y2KNQtb7iXefs9zNvP55ETe+GgOc1d7LlTUo+P9\nRDbtiF4XgE4XQL2buhYbaTL62VeY+OJrHv1BtWvXJrJud/zsgegVP8J0LVgwxrciiGPmjyIv8wx2\nezYZ5/d6DC0GWLdtE1OWx7Bxp+dyuaP+PQ5jYBQ64YfBP4Lnnn7FY7sut7Znav9oj3v4AO9N/5hQ\n2Ri94ofRHkzjmx4v38CKIe7UYbb88BGKLQO7NY316xdxPjnRrZ3JZGLOqgVM/+gtj0oNQUFB3Hxn\nD/S6IHQ6f0Lq3lJsGefBj77I5L5ji5XVv6lNb/ztoegVP4KVBsx8reRtyrISvWgi2ZcOYbdnk3v5\nJOMWenbRbj+0mynLY/j4e8/byLOGvEFAeBN0wojeEMpd93ouQNe8YVMm9X29WEmbmFemU8OvFXrF\niEEJICLibp8aE4DYZZOw5aVgt2VxcMd/ypSl7yuqyocyG1grhOgPxANPAQgh2gGDpJQvOY4bA9cD\nRW/JVwkh6qDuDsQBvilO7iWvL4gm7ewepIDlh7fyUVAdhGKjRt0bmDNMvTh1b9PVJ7Wlu9zajv/U\nb0du2kX8gmvyfx29lxjxhv1//uF0oALYzalumfJWm5Xpbw/DZlW35wbs34rOz4gQOu6972ln1rwn\npeXy0KP7E3z1dRqK3Uqjm3z7owOwFqlVknLhlFubOasWcGLvt0jsnDu8hY0bPwFpwz8sgphhcwgK\nCuKGug2JnfGpT/pUt3lnUuKPoTMG0KuUaoplZcuerS45JnYllx//+MUtWfS12YOx5Khh1hMO70Bn\nDEEIyU23dXEajlHPDAEfZNA/dc9jLDh7FLs5m/B6TXxeFjr9kuvWnDnNvYZNWnYGmza+hSItJBze\nwq/bNiCkgj4ghOEvjnWu1Bd7EYDjDS3a3MuxA+rv5r6uvq3mGXfqMDZroSRS7Oze/1ul6/JVyQpF\nSpkqpbxPStnM8f9lx/k/8o2J4/iMlLK+lIWcEer5e6WUraSUt0opn5dSZhd9jytJ+vk/CyKVlIvk\nZh/Fak4m5czuYu928zmfnMiQaf0ZGN2LV2YOcktCK8ro+WPITDyI1ZyMKfU405aUTQ+pNOrVbYgo\n5EbTGYLdt6EyUp3GRFFsZFoPYsm5gCUnkR82Li1Vd2zx1ysYOL43A8c9zZuflV67Y+2aeeRlJ2DN\nTeJk3Hd8utm30eE6f9d95AAP9WlOH97pNLQ2JZ3sHHUOspOOEv3umFLf49XZrzAwuhdWbg6mAAAS\n20lEQVRDpvTl0NljJbadu3oRF49tw2pOIi8zntgPfKs71arpLS5F13QYuOMm15rv6TmZTmMipSTb\nfhxLzhmspkv8uWt9sau0fDbu/FHVZYt+iuhFpYsSzn9/Kub001jNySSf3sXU5RWXLyqMf4hrPRp9\noHt9k+ysDGe0n03JITN3P1ZzMub008x7v3Tl6uhFExkY/RSDJj7n1efz5671WE2XsOQksmlDbKm/\n/bLQvG5j9LoA57FA0Pj6yq8pr2XKl4PCQox2nQVH4WcALhep3FeUmNjJ5GXGq1m+l08ydUnJse85\naZdcjm05Fc/YLsyoZ4ZQq1E79IZQDP4R3P+I+z62ThR8TaS0IoUkf8yKYmbHseJLAhw6e4y4bV84\ns3yP793E5r3bim1/PP4kdlvB/YFE4dCx/eUYWfEM7BuNMbgeekMogbWaMmOQ+wXcZY6FhcI/FUtm\nyXMwZsE4cpKPqcKE2QksXl7yTca5c67VAu15vrvQgLpabtr6AQx+NTD4hXNz+/9z0/Ey6A3OGwsp\n7SjCTv4cS2ln79GSs+b/uy4Wa24SNms6Kad2Mf+Lkgu72c2uY0y5eKZMYyqNqUOmExB+A3pDKP4h\nDRjRb7x7o0Ih6XaZhyg0CXZzyYoU89YsIeXULqeo6oZ1Jcvq//HnHlc9NcXMrlI+07IQFBREp3uf\nw2Cshd4vjMgbO6nRf5WMpuVVDprf0Z1jf3yDgg1/pQZ6URDBUjPq+hKeCbYi8g6W7JLLiAbVjHTe\nOQIYgiuWzesJT3H8hYmqFYl/6PXkZZ1DCD8CbREIvXqB1ekC6FzCttTeo/tds3ylnbhj+4vdDmze\nsCl6fbAz/FggaHmjb6s9tm7SslTNpnvv7+1MOvWToS4VKI1hJeeJZKe7bqnZzSVLqdRv0JTjFw87\njYrev2IilJ5QtZ9GFvt4SEAQode1IuvCQYTQE2irhU6vqhsLoadNCVndJpMJuzXLeV8lBSScd99G\nLIw+IBx7IbWJiLqNSmhddmqGhrN40rIS29SuVQe9Xxh2ayZ+IgiDDHKOwVCKWkHChSKiqrbsEsPt\n293SlvNH/uc0KjpdAG2a3OaxbXnp91Bvj5p3lYlmUMrB6Gdf4Xz3JzmRcIqaweG8v/od7HnZhEU2\nLrUGgX+NSGzJBXc/obUblNh++sBpjF8yAfPlixiCa3isFlgZLJn6Ib8d2UNoUAg/7t7CsbhtoNNz\nX/enS1REvbdNV7Zt/qSQpIeRbu1K9i098dSrfP3flUi7lQbN2xbrzL6SPHV3D7q36cKeEwe5uf6N\nzFkxE0tmKsawiFLVCiLq3cCFzIKVql9InRLbj/nXq0zITCP13FF0foH0L5KvVFnMHz2PQ2ePkWcx\nc+rCOX7avBYUO81vu6tENeegoCD0/rWcqsICHbeUIisy8uWpvLMiBsWSQ1jUDRUWVy0PgcYA3pn8\nCbuO7+OW629i7qp5ZF46jd4/hOF93ZNUC9OyeWt+O3+gkKhqrRJzt3p0vJ/jZ09w/MCvoNPzwD97\ne60kfC2hVWwsIxUNvTOZTExYMglzZgqhtevz5quzKtSfyqCiY96480c2ff85KApd73m0TFIqVYEv\nwisnvDeNy4mnMQbVYOqQaRWWvr/SVHTMh84e471P56NYzDRp0Y7Rz3qOeLuaqOiY565exKmjf6Az\nBjDo+ZHFRrRdLWgVG6shQUFB11yd6IrSo+P9ZapXUh0oqodW3bm10U0smlCy36S6cS0YzcpGMyjX\nGCaTyblEL27JvHrLOnbv2U6NmnU86ktda5y+GE9CysUSFVQnvz+TrIzL/KPDfVUi2+1rth/aTf3a\ndYstW7v90G7W/fgFfsYAxr7gOafnWiIpNYU/zx2jQ/M7it06mrt6EQkJp2ne/DYGP+qeJHytsefE\nfvyNAcWubE5fjCd2TSwSycs9X65w8a/KQDMo1xC/HdnDio9mYrdlodP5c+fdz7hFcqz49nN+/ekj\npLSTmQivxpzn3XGekxGvBSa8N41LJ3YgUVgVVJe3xy11u+DkK+kCbFp/GJuiVLiiZFWRlJri1MsS\nQk+Dm+9xuyn47cgeVq6c7vRLjZ/zis/yYaqCheuWc+DX/6BIK5/7hTFs8Cy3i2z0oomknNqFFLAn\n8SAxGZfdikxdSwydPhBz+imEhNDrWrkVgkvLSmf2O69hs6rRcHMXH2fy6MVu1VqvNrSw4WuIVV8u\nc1ZkU5Q89vy60a3N3n3bXMITzZfPVlr/fI3JZOLSid+cjk+L6SIxn7pXYMxLK3CAK9jYvqvknICr\nmXmr33GKL0ppJ+GIe4j1xl82upR7teRe4nj8Sbd21woHd21yRgLarJks+2yhW5u0hOPOqCqJJP5E\npZU/8jmLv16BOV2NgpMCsi4cZPuh3S5t1m//zmlMQFXF/s8vGyq1n+VBMyjXEEWVa+2WTF6ZOYjo\nxZOc54yBRbYL9K5FhK4l8uwWKFQpEilJOn2IYbMGu0hlCJ3rGAMCyyeAdzVQVLnWbs9l2KyhjJz7\nmjOBtGYN17BlgZ46Nb2XvL/qKKIEbE5P4JWZg1xkfnQGf5c2er8ArlVyTTkuxwo2Vn0Ry/A5w9hz\nQs25alzvekSh/DYhoWEpKQlXA5pBuYbo2PEBdI6a1Io9D5s9k9zLJ0k+tZNhMUMBmPjSBIzB1yHQ\nodcH0+nup6qyyxWiZmg4gREF+8Y2mY3VnIQp9S+2/vAhX/yirtDu6NITvT4QgQ7/kAZM6e9bNYHK\n5F89nkevV/OapGLFLnMxpR4nM/EAk98ZAajO4KDazRFCj07nT+Pb7r+mfSi1GrVyXjztSg5WSxq5\nl09y9PeviflELdr6ZM8BGPzCEOgw+Ecw6IVRVdnlCtG/x/MYjKo6g5QKNmnCkn2e7KSjLPtgGkmp\nKXRrfRe1m3RAJwzoMFCzUdtrwjeo+VCuIV544Gmuq1OPbfu2k3jqIHbH1ghAXppa7rdmaDixb6ys\nqi76nIXjY1nw5VJSLieTcGQLQqfeA0ns7Nz7C0/d3UN10FYDJy2o5XmHDJrN+i0buHTuBJasgu08\nmynJmTxXWs2Xa4nZQ6ez4tvPOXPhNAlHdiAdUv5SwLlTqu6rr7TxrgZqhoYzffQSlv53BcmX4sm+\ndMT5mM2WxXd//MwLDzzN7KHTq7CX5UNboVxjdG/TlWn9xxFc07XUjM6vckt9VibDew1k+oCJGPyC\nXc6H1yx/nfermdZNWjK1fzQNm7qW8hU6/0ov6VpZ9HuoN9P6j8MQ4Kq5ZQx21+CqDkRG1GZS39e5\nt+sjLtJGAj23NLm681lKQjMo1yiTB07GP6whel0ABv8Iej05uKq7dMXp1O1fGPzC0OkCCIlsWenl\nTSubcX1GEhLVEp0uAL1fGB3vqVpZjcrgmV5DMPhHoNcF4B96PVMGVu98nsc7P0jd5l3Q6wPR64Np\n1Oo+2ja7vaq7VW60La9rlJqh4W6lSKs7V4NWUWXjTV376kS31ne5lRSu7swYOLmqu+AztBWKhoaG\nhoZP0AyKhoaGhoZP0AyKhoaGhoZP0AyKhoaGhoZPqBKDIoR4SgjxpxBCcdSRL67dg0KIY0KIv4QQ\n0YXO3yCE2CWEOCGEWCOEuHbTwTU0NDSqCVW1QjkEPAFsLa6BUGuwLgYeAloCzwoh8uuWzgHmSymb\nAWlA/yvbXQ0NDQ2N0qgSgyKlPCKlPFZKs/bAX1LKU1JKC/A58JgQQgD3Al862q0EHr9yvdXQ0NDQ\n8Iar2YdSHzhX6Pi841wEkC6ltBU5r6GhoaFRhVyxxEYhxGagroeHJkgpv/bmJTyckyWcL64fA4AB\nAFFRUfzvf//z4q2LJzs7u8Kvca3xdxvz3228oI3570BljPeKGRQpZfcKvsR5oLBecwPgApAChAsh\nDI5VSv754vqxDFgGIIRI7tatW0ULhNR29OHvxN9tzH+38YI25r8DFRlvI28aXc3SK78DzYQQNwAJ\nQG/gX1JKKYTYAvRC9au8CHiz4kFKWaeinRJC/CGlLDYyrTrydxvz3228oI3570BljLeqwoZ7CiHO\nA52Ab4QQ3zvOXyeE2ATgWH28AnwPHAHWSin/dLzEWGCUEOIvVJ/K30vUSkNDQ+MqpEpWKFLKdcA6\nD+cvAA8XOt4EbPLQ7hRqFJiGhoaGxlXC1RzldbWyrKo7UAX83cb8dxsvaGP+O3DFxyukLDZASkND\nQ0NDw2u0FYqGhoaGhk/QDEoZKE5brLoghLheCLFFCHHEobU23HG+lhDiR4d22o9CiJpV3VdfI4TQ\nCyH2CSE2Oo6rrV6cECJcCPGlEOKoY647Vfc5FkKMdHynDwkhVgshAqrbHAshPhRCJAkhDhU653Fe\nhcq7jmvZASFEG1/0QTMoXlKKtlh1wQa8JqW8GegIDHWMMRr4yaGd9pPjuLoxHDWaMJ/qrBe3APhO\nStkCuB113NV2joUQ9YFXgXZSylsBPWoaQnWb44+AB4ucK25eHwKaOf4NAGJ90QHNoHiPR22xKu6T\nT5FSJkop9zr+zkK90NRHHedKR7Nqp50mhGgAPAJ84DiutnpxQogw4B84Qu2llBYpZTrVfI5RI1oD\nhRAGIAhIpJrNsZRyK3C5yOni5vUx4GOpshM1WbxeRfugGRTvKU5brFoihGgM3AHsAqKklImgGh0g\nsup6dkV4BxgDKI7j6qwX1wRIBlY4tvg+EEIEU43nWEqZAMwF4lENSQawh+o7x4Upbl6vyPVMMyje\nUyYNsWsZIUQI8B9ghJQys6r7cyURQvQAkqSUewqf9tC0usy1AWgDxEop7wByqEbbW55w+A0eA24A\nrgOCUbd8ilJd5tgbrsh3XDMo3lOctli1Qgjhh2pMVkkpv3KcvpS/HHb8n1RV/bsC3AU8KoQ4g7qN\neS/qiiXcsT0C1WuuzwPnpZS7HMdfohqY6jzH3YHTUspkKaUV+AroTPWd48IUN69X5HqmGRTvcWqL\nOaJBegMbqrhPPsXhO1gOHJFSziv00AZUzTQog3batYCUcpyUsoGUsjHqnP4spXwOyNeLg2o0Zinl\nReCcEOImx6n7gMNU4zlG3erqKIQIcnzH88dcLee4CMXN6wbgBUe0V0cgI39rrCJoiY1lQAjxMOrd\nqx74UEo5s4q75FOEEF2AbcBBCvwJ41H9KGuBhqg/zqeklEWdf9c8Qoh7gNFSyh5CiCaoK5ZawD7g\neSllXlX2z1cIIVqjBiAYgVNAP9Sby2o7x0KIacAzqJGM+4CXUH0G1WaOhRCrgXtQVYUvAVOA9XiY\nV4dhXYQaFWYC+kkp/6hwHzSDoqGhoaHhC7QtLw0NDQ0Nn6AZFA0NDQ0Nn6AZFA0NDQ0Nn6AZFA0N\nDQ0Nn6AZFA0NDQ0Nn3A115TX0KgShBARqEJ6AHUBO6pcCUB7h5abr97r38AmR35IWZ73DRAmpezq\nq75oaFQUzaBoaBRBSpkKtAYQQkwFsqWUc6/Q2/0b2At4bVAcBq8VYBZCNJRSxl+hvmlolAlty0tD\nw0uEEOOFEEMcfy8UQvzg+PsBIcRHjr8fEkL8JoTY66ixEew4f6cQ4hchxB4hxLdCiCghxDOohmuN\nECJOCGEUQrwlhDjsqFExp5iu9EJNWFuDmqyX379mjvoeu4UQ04UQ6Y7zOiHEPEctkINCiF7FvK6G\nRoXQDIqGhvdsBfK3mNpQoAXVBdgmhIhEFVq8T0rZBjgADBdC+KPWIHlSStkW+BSYLqVcA8QBz0gp\nWwM1gYeBW6SUtwExxfTjWWC149+zhc4vBOZKKdujZkrn8xRqDZ/bgfuB+Y6+amj4FG3LS0PDe34H\n7hRChAPZwF+oEv9dgU9QBQdbAjtUZQuMwHbgZuAWYLPjvB5VnK8ol1Elb953+Eg2Fm3gKBbVENgp\npZRCrTTZQkp5FOiAapAAPgNmOP7uAnwmpbQDF4UQ24F2wKYKfBYaGm5oBkVDw0uklHlCiAvAC8Cv\nwHFUocGGUsrjQohbUCsh9in8PCHEHcCB0hzoUkqrEKId6iqiNzAY+GeRZs+g1ms57TBONRxtp5bw\n0p6kyjU0fI625aWhUTa2AqMd/28DhqIWawLYAdztEJZECBEshGiGqmxbXwjR3nHe6DA+AFlAqON8\nKGrk1kZgJOrqpyjPAt2llI0dCsntKdj22g30dPzdu0ifeztWM1Gokv0VFgLU0CiKZlA0NMrGNiAK\n2OWoBGh1nENKeQm1LvkaIcR+VAPT3KFg2wuY5zi/D3V7CmAF8IEQIg7Vh/KNo83PwKjCbyyEaIoa\nxuw0BlLKE0CeEKItat30sUKI3aiV+TIczb4EjgL7gc3AKClldap3onGVoKkNa2hUExwRZSaHb+V5\noKeU8smq7pfG3wfNh6KhUX24E3hHCKED0lDrnGhoVBraCkVDQ0NDwydoPhQNDQ0NDZ+gGRQNDQ0N\nDZ+gGRQNDQ0NDZ+gGRQNDQ0NDZ+gGRQNDQ0NDZ+gGRQNDQ0NDZ/w/zJ34DmwHiDpAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd678e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot and Formatting\n",
    "x = Major_News_Sentiment['Tweets Ago']\n",
    "y = Major_News_Sentiment['Compound']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for news in ['BBC', 'CBS', 'CNN', 'Fox', 'NYTimes', 'Economist', 'MSNBC', 'ABC', 'NewsWeek', 'WashPost']:\n",
    "    ax.scatter(x, y, s=20, c=np.random.rand(3,), label=news,\n",
    "               alpha=0.5, edgecolors='none')\n",
    "plt.xlabel('Tweets Ago')\n",
    "plt.ylabel('Compound Score')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "\n",
    "fig2.savefig('TweetSentiment.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 3 Observable Trends\n",
    "\n",
    "# 1. New York Times' twitter account is relatively more negative than other news outlets\n",
    "# 2. News Outlet twitter accounts tend to be negative in nature but to varying extents\n",
    "# 3. ABC and The Economist represent the least negative news outlets when considering their last 100 tweets"
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