Text Data Analysis Project Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "brutal-interview",
   "metadata": {},
   "source": [
    "# 신문사 성향 별 대한민국 정치흐름 파악"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equipped-luther",
   "metadata": {},
   "source": [
    "<font color='CC3D3D'>\n",
    "    \n",
    "[라이브러리 불러오기](#라이브러리-불러오기)  \n",
    "[데이터 수집](#데이터-수집)  \n",
    "[전처리](#전처리)  \n",
    "[단어 빈도](#단어-빈도)  \n",
    "[감성 분석](#감성-분석)  \n",
    "[주제 분석](#주제-분석)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "foreign-bennett",
   "metadata": {},
   "source": [
    "#  <font color ='CC3D3D'>라이브러리 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "functional-polls",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt\n",
    "from matplotlib import font_manager, rc\n",
    "%matplotlib inline\n",
    "\n",
    "import platform\n",
    "your_os = platform.system()\n",
    "if your_os == 'Linux':\n",
    "    rc('font', family='NanumGothic')\n",
    "elif your_os == 'Windows':\n",
    "    ttf = \"c:/Windows/Fonts/malgun.ttf\"\n",
    "    font_name = font_manager.FontProperties(fname=ttf).get_name()\n",
    "    rc('font', family=font_name)\n",
    "elif your_os == 'Darwin':\n",
    "    rc('font', family='AppleGothic')\n",
    "rc('axes', unicode_minus=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "julian-plaza",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import lxml.html\n",
    "import re\n",
    "import stanza\n",
    "import joblib\n",
    "import sklearn\n",
    "from wordcloud import WordCloud\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from selenium.webdriver import Chrome\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.decomposition import NMF\n",
    "from gensim.models.ldamodel import LdaModel\n",
    "from gensim.models import CoherenceModel\n",
    "from gensim.matutils import Sparse2Corpus\n",
    "from gensim.corpora.dictionary import Dictionary\n",
    "from factor_analyzer.rotator import Rotator\n",
    "import pyLDAvis.gensim\n",
    "import tensorflow as tf\n",
    "import openpyxl\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subtle-missile",
   "metadata": {},
   "source": [
    "# <font color ='CC3D3D'>데이터 수집"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "linear-syria",
   "metadata": {},
   "source": [
    "### 보수(우파) 언론사\n",
    "- 조선일보\n",
    "- 중앙일보\n",
    "- 동아일보\n",
    "- 한국경제"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "large-afghanistan",
   "metadata": {},
   "source": [
    "### 1. 조선일보"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "maritime-given",
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = Chrome()\n",
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "browser.get('https://www.chosun.com/opinion/editorial/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "verbal-promise",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in browser.find_elements_by_css_selector('div#main div.story-feed div.story-card a.text__link span'):\n",
    "    sheet.append([f'{i.text}'])\n",
    "wb.save('chosun.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forced-bishop",
   "metadata": {},
   "source": [
    "### 2. 중앙일보"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "metropolitan-acceptance",
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = Chrome()\n",
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "for page_num in range(1, 38):\n",
    "    browser.get(f'https://news.joins.com/find/list?page={page_num}&IsDuplicate=False&key=EditorialColumn&Display=%EC%82%AC%EC%84%A4&Keyword=%EC%82%AC%EC%84%A4&SourceGroupType=all&ServiceCode=20%2C34&SourceCode=1%2C61')\n",
    "    for i in browser.find_elements_by_css_selector('div#content div.list_basic li h2 a'):\n",
    "        sheet.append([f'{i.text}'])\n",
    "wb.save('JoongAng.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "superb-begin",
   "metadata": {},
   "source": [
    "### 3. 동아일보"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "latin-adoption",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "browser = Chrome()\n",
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "for page_num in range(1, 1202, 20):\n",
    "    browser.get(f'https://www.donga.com/news/Series/70040100000001?p={page_num}&prod=news&ymd=&m=')\n",
    "    for i in browser.find_elements_by_css_selector('div#content a span.tit'):\n",
    "        sheet.append([f'{i.text}'])\n",
    "wb.save('donga.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hairy-spyware",
   "metadata": {},
   "source": [
    "### 4. 한국경제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "corporate-chick",
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = Chrome()\n",
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "for page_num in range(1, 62):\n",
    "    browser.get(f'https://www.hankyung.com/opinion/0001?page={page_num}')\n",
    "    for i in browser.find_elements_by_css_selector('div#container li h3.tit a'):\n",
    "        sheet.append([f'{i.text}'])\n",
    "wb.save('hanguk.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "molecular-internship",
   "metadata": {},
   "source": [
    "### 좌파(진보) 언론사\n",
    "- 한겨례\n",
    "- 프레시안\n",
    "- 경향신문\n",
    "- 머니투데이\n",
    "- 이데일리"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "green-portuguese",
   "metadata": {},
   "source": [
    "### 1. 한겨례"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adverse-trail",
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = Chrome()\n",
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "for page_num in range(1, 82):\n",
    "    browser.get(f'http://www.hani.co.kr/arti/opinion/editorial/list{page_num}.html')\n",
    "    for i in browser.find_elements_by_css_selector('div#section-left-scroll-in div.section-list-area div h4 a'):\n",
    "        sheet.append([f'{i.text}'])\n",
    "wb.save('Hangye.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appropriate-truth",
   "metadata": {},
   "source": [
    "### 2. 프레시안"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "honest-absolute",
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = Chrome()\n",
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "for page_num in range(1, 55):\n",
    "    browser.get(f'https://m.pressian.com/m/pages/opinion-column?page={page_num}')\n",
    "    for i in browser.find_elements_by_css_selector('#container div div.arl_026 ul li div.box p a'):\n",
    "        sheet.append([f'{i.text}'])\n",
    "wb.save('Presian.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "warming-doctrine",
   "metadata": {},
   "source": [
    "### 3. 경향신문"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "endangered-boutique",
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = Chrome()\n",
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "browser.get('https://m.khan.co.kr/list.html?type=opinion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "lonely-buyer",
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0\n",
    "while (i<=582):\n",
    "    try :\n",
    "        browser.find_element_by_css_selector('button.link_corp.btn_more').click()\n",
    "        i+=1\n",
    "    except:\n",
    "        pass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "searching-storm",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "con = browser.find_elements_by_css_selector('ul#itemsBody.list_df li')\n",
    "\n",
    "for i in range(len(con)):\n",
    "    try: \n",
    "        if con[i].find_element_by_css_selector(\"#itemsBody li a div.cont_thumb em\").text == '사설':\n",
    "            title = con[i].find_element_by_css_selector(\"#itemsBody li a div.cont_thumb strong\")\n",
    "            sheet.append([f'{title.text}'])\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "wb.save('Kyunghyang.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polar-america",
   "metadata": {},
   "source": [
    "### 4. 머니투데이"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ordered-mainland",
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = Chrome()\n",
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "for page_num in range(1, 16):\n",
    "    browser.get(f'https://news.mt.co.kr/column/opinion_inside.html?category=03&page={page_num}')\n",
    "    for i in browser.find_elements_by_css_selector('#content ul.conlist_p1.mgt20 li div strong a'):\n",
    "        sheet.append([f'{i.text}'])\n",
    "wb.save('moneytoday.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sharing-scotland",
   "metadata": {},
   "source": [
    "### 5. 이데일리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bronze-lease",
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = Chrome()\n",
    "wb = openpyxl.Workbook()\n",
    "sheet = wb.active\n",
    "browser.get('https://www.edaily.co.kr/opinion/editorial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "novel-cinema",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in browser.find_elements_by_css_selector('#newslist > li > dl > dd:nth-child(2) > span'):\n",
    "    sheet.append([f'{i.text}'])\n",
    "wb.save('edaily.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exempt-disco",
   "metadata": {},
   "source": [
    "# <font color ='CC3D3D'>전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "lesser-withdrawal",
   "metadata": {},
   "outputs": [],
   "source": [
    "chosun = pd.read_excel('chosun.xlsx', names=['기사'], header=None)\n",
    "JoongAng = pd.read_excel('JoongAng.xlsx', names=['기사'], header=None)\n",
    "donga = pd.read_excel('donga.xlsx', names=['기사'], header=None)\n",
    "hanguk = pd.read_excel('hanguk.xlsx', names=['기사'], header=None)\n",
    "Hangye = pd.read_excel('Hangye.xlsx', names=['기사'], header=None)\n",
    "Presian = pd.read_excel('Presian.xlsx', names=['기사'], header=None)\n",
    "Kyunghyang = pd.read_excel('Kyunghyang.xlsx', names=['기사'], header=None)\n",
    "moneytoday = pd.read_excel('moneytoday.xlsx', names=['기사'], header=None)\n",
    "edaily = pd.read_excel('edaily.xlsx', names=['기사'], header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "stainless-fundamentals",
   "metadata": {},
   "outputs": [],
   "source": [
    "chosun['신문사'] = '조선일보'\n",
    "JoongAng['신문사'] = '중앙일보'\n",
    "donga['신문사'] = '동아일보'\n",
    "hanguk['신문사'] = '한국경제'\n",
    "Hangye['신문사'] = '한겨례'\n",
    "Presian['신문사'] = '프레시안'\n",
    "Kyunghyang['신문사'] = '경향신문'\n",
    "moneytoday['신문사'] = '머니투데이'\n",
    "edaily['신문사'] = '이데일리'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "green-organic",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 라벨링\n",
    "chosun['성향'], JoongAng['성향'], donga['성향'], hanguk['성향'] = 1,1,1,1  # 보수\n",
    "Hangye['성향'], Presian['성향'], Kyunghyang['성향'], moneytoday['성향'], edaily['성향'] = 0,0,0,0,0  # 진보"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "egyptian-walnut",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df = pd.concat([chosun, JoongAng, donga, hanguk, Hangye, Presian, Kyunghyang, moneytoday, edaily], axis=0)\n",
    "df = df[['신문사', '기사', '성향']]\n",
    "df = sklearn.utils.shuffle(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "lyric-attendance",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 불용어 처리\n",
    "stop_words = []\n",
    "for i in df['기사']:\n",
    "    stop_words.append(i.replace('[사설] ', '').replace('[사설]', ''))\n",
    "df['기사'] = stop_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "direct-canon",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>신문사</th>\n",
       "      <th>기사</th>\n",
       "      <th>성향</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>조선일보</td>\n",
       "      <td>폭우 피해도 4대강 사업 탓, 전 정권 핑계 댈 건가</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>이데일리</td>\n",
       "      <td>대한민국이 ''성범죄 조장국''으로 눈총받는 까닭</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>동아일보</td>\n",
       "      <td>이란 억류 선원 석방, 진짜 난제는 지금부터다</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>동아일보</td>\n",
       "      <td>與 “全 공직자 재산등록 검토”… 展示용 과잉대응</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>경향신문</td>\n",
       "      <td>코로나가 드리운 또 하나의 그늘, 사교육비 양극화</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8451</th>\n",
       "      <td>동아일보</td>\n",
       "      <td>한국당 黃대표-텃밭 의원들, 기득권 집착해 공멸하려 하나</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8452</th>\n",
       "      <td>머니투데이</td>\n",
       "      <td>총선 1주일, 한가한 정부</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8453</th>\n",
       "      <td>조선일보</td>\n",
       "      <td>직업·소득 없던 김홍걸의 100억 재산, 어디서 나왔나</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8454</th>\n",
       "      <td>프레시안</td>\n",
       "      <td>혼돈 세상과 새로운 표준, 코로나가 나눌 4가지의 계급</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8455</th>\n",
       "      <td>동아일보</td>\n",
       "      <td>후보들 이어 당 지도부까지 막말… ‘깜깜이 선거’ 노린 구태 심판해야</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8456 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        신문사                                      기사  성향\n",
       "0      조선일보           폭우 피해도 4대강 사업 탓, 전 정권 핑계 댈 건가   1\n",
       "1      이데일리             대한민국이 ''성범죄 조장국''으로 눈총받는 까닭   0\n",
       "2      동아일보               이란 억류 선원 석방, 진짜 난제는 지금부터다   1\n",
       "3      동아일보             與 “全 공직자 재산등록 검토”… 展示용 과잉대응   1\n",
       "4      경향신문             코로나가 드리운 또 하나의 그늘, 사교육비 양극화   0\n",
       "...     ...                                     ...  ..\n",
       "8451   동아일보         한국당 黃대표-텃밭 의원들, 기득권 집착해 공멸하려 하나   1\n",
       "8452  머니투데이                          총선 1주일, 한가한 정부   0\n",
       "8453   조선일보          직업·소득 없던 김홍걸의 100억 재산, 어디서 나왔나   1\n",
       "8454   프레시안          혼돈 세상과 새로운 표준, 코로나가 나눌 4가지의 계급   0\n",
       "8455   동아일보  후보들 이어 당 지도부까지 막말… ‘깜깜이 선거’ 노린 구태 심판해야   1\n",
       "\n",
       "[8456 rows x 3 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(drop=True, inplace=True) ;df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "offshore-walker",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df.groupby('신문사')['기사'].count()\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(p, marker=\"o\", c=\"orange\", lw=3, ms=8, mfc=\"w\", mew=3)\n",
    "\n",
    "plt.text(x=0.2, y=1349, s=1349, fontsize=15)\n",
    "plt.text(x=1.1, y=1225, s=1205, fontsize=15)\n",
    "plt.text(x=2.2, y=370, s=370, fontsize=15)\n",
    "plt.text(x=3.2, y=666, s=646, fontsize=15)\n",
    "plt.text(x=4.1, y=1215, s=1195, fontsize=15)\n",
    "plt.text(x=5.1, y=759, s=739, fontsize=15)\n",
    "plt.text(x=6.2, y=553, s=533, fontsize=15)\n",
    "plt.text(x=7.1, y=1232, s=1212, fontsize=15)\n",
    "plt.text(x=8.1, y=1227, s=1207, fontsize=15)\n",
    "\n",
    "\n",
    "plt.title(\"신문사 별 사설 발행 부수\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fitting-works",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-04-29 18:26:05 INFO: Loading these models for language: ko (Korean):\n",
      "=======================\n",
      "| Processor | Package |\n",
      "-----------------------\n",
      "| tokenize  | kaist   |\n",
      "| pos       | kaist   |\n",
      "| lemma     | kaist   |\n",
      "| depparse  | kaist   |\n",
      "=======================\n",
      "\n",
      "2021-04-29 18:26:06 INFO: Use device: gpu\n",
      "2021-04-29 18:26:06 INFO: Loading: tokenize\n",
      "2021-04-29 18:26:08 INFO: Loading: pos\n",
      "2021-04-29 18:26:08 INFO: Loading: lemma\n",
      "2021-04-29 18:26:08 INFO: Loading: depparse\n",
      "2021-04-29 18:26:09 INFO: Done loading processors!\n"
     ]
    }
   ],
   "source": [
    "nlp = stanza.Pipeline('ko')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "purple-naples",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_nouns(text):\n",
    "    doc = nlp(text)\n",
    "    for sentence in doc.sentences:\n",
    "        for word in sentence.words:\n",
    "            lemma = word.lemma.split('+')\n",
    "            xpos = word.xpos.split('+')\n",
    "            for lem, pos in zip(lemma, xpos):\n",
    "                if pos.startswith('n'):\n",
    "                    yield lem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "complimentary-danger",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = ['정부', '국민', '대통령', '것', '만', '년', '수', '때', '일', '차', '1', '2', '3', '코로', '당코로', '오동희']\n",
    "cv = CountVectorizer(max_features=2000, tokenizer=extract_nouns, stop_words=stop_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "least-notion",
   "metadata": {},
   "source": [
    "# <font color ='CC3D3D'>단어 빈도"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dress-removal",
   "metadata": {},
   "source": [
    "- 전체신문사 단어구름"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "numeric-chain",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:388: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['당코', '코'] not in stop_words.\n",
      "  warnings.warn('Your stop_words may be inconsistent with '\n"
     ]
    }
   ],
   "source": [
    "tdm1 = cv.fit_transform(df.loc[:,'기사'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "hired-carrier",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "word_count = pd.DataFrame({\n",
    "    '단어': cv.get_feature_names(),\n",
    "    '빈도': tdm1.sum(axis=0).flat\n",
    "})\n",
    "\n",
    "word_count.sort_values('빈도', ascending=False)\n",
    "count_dic = word_count.set_index('단어')['빈도'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "bearing-tokyo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z+vq5655taq+2sLK5NlKHk+TMoFD5AZlAMM5yUYk2JJSt7NygTID1VjtGv7rlCEJAAAWtNo5LDW5jfya9/Hhk/myRfNGd6GjL7NPccoNBi+6GMubb7mPn9tB0VT4GQQBQAlrXEsVFpf88/7ibr93qneMxDP9u/F+bF578bvtY+TDNCAgoCvJ/HV1+pDIMe5Ke9ufb8B7uHr/27H34XeSEfy4sbtsuxN6+buUfN48wojMcLv3Oyx+0VPhxxdnoiLSGM1gjD5yOzy804saAjbr3fPxsrc9Or/Fcufw+7mLUh5TiUp5IjH0x0iiCMCkUL2uLMaEBvZqpLinIrRUAVHxxAaMSSEJZwwZLUwkaJxwm2CVEEdSKYlf/duS0snP4OFOb7dOfX7sZttGjgIDQOjs9NPQuEsru5fZBZzUmycPfcp0x3VJiT8KDE6kv+dJaMyAKgdTXvsVPLYfWra88LcJk2OPr0U9vvM9KKyrOKydTiFb2ZgFt3HuPaH0jfrPyXaWFlUe23npyI3rolI4TFvUmEFAA+PnvGT9MO7Ri3L4V28bYOVuoPklvpnb88l4hEtSG7GA4PvD0yQAbm1979nE3MwOAqUEhXVzdD0VFrn30YO+Agc4crtFP1x8yhSTD1I9AhTLJvDfHo0rSZXitOQGXTF/p37+vQ4A+7cdkaxSK0I4+EePvs3IVx1djPq28dEdtNaFEWlTFe5WSsXPkgLo2S4VX/VdIsIYNCyWhGu1SE3Jr+B96IsVlB5OfYrjqxFkkk1zOjJrp1cWBbqbxXonsxxmH05MLRszsPPP7QRbWbJFIkpVS+PBK1LzBf0xa0mf49M6Kyuf+elRaVPnntcUOblaKQjaXvuXErJ3fX/zn8NO569QYR0PZOLSX2nIUQW6OU/VBczcz29Rd41CxkXlZmPS2OLVueZmYf/jzMz0NVqNxLjJWZ50zkbE6DRaVQKQSvprd+J/B+vchkErqWisFVVI1UmIKbp4Jj3+fuffGEusv4SYUGqlZsEuzYJcWrdy2LDndppuf0xfH7pmrByKomiUjKo284vcxuIZxx/8fKiXqfXQAoFKi7V9BGTaVoph2mRw2tcbDJqmgWGf9+LwC7RVGBq2pLOPLj4fN6Dp99cCo54mpn7KHzegKAFKpbJDPCvkX4+DDVQ5uVu9eJKbEVV+VUykRFgirPFjVnpXXcm7LcG1DRQJCGGhfIzn3P4PVpHlbkNXK2tGEDaYn5nk0s7dWFxzXvncLHD+VGp+rMFhqrZUC7VebIAiCoChCIBq2kUI0sL6hXJkzIadcd0SncdhzauL4qkQ1ZnHdgO5e1tXT+U+5hRtvVa+Llwt0mM5z76qFUqOeJUS/TIJq5cjq1yeRSLj5udbqs0yKqQiQdb71uxiTRQxcKV+oohNpOg2W8ul/xGC9Kvz8w/uLhcJaep4ognSx8d0WMlquoFYkquxx7zct3hV0IvnHgCF97Fvo+dBlkWfs6WZL/Ho/yvu0Mfbqg57fKS4VCCvmhB9LrsxXucWXY/dH6Lixt8+Jta4CkFHio6EzAGDErZNpk77TUtNQXH1sn92KKS2qMrNkqlyKeJqAIIiHn5r17P8GKIrcfrEGAObtubR8RBcX61oT54E/Hrn2U/WGJo7D8QeRZx5HC8SSHkFei7/pJC8f9OORyz9ORk1qqe04LDtOA+63KFBeiRsZ0kLhtBjoaK8wWDJM3w36m6dejfq21tz81ulXO1efVy4J7eI3eHLH2vcBnUgWiHiZvFIvtjUAdLfuDIZgYoNVLtE97KxLZEnSL3FnLnT4AQAuZb14kP9ud4gBgg0AsD/pkYq1AgAMxx/mfXpTnNrW0gMALCms6AHrtTSyOOL084JE/Q2WFK/WOZHimLT2HO1CRkRdawUA8eW5F9Lf7u48SJN289Cbxy/1m1BvtwyN9B3V5vX9j0tG7h41q2tgey9zK7ZEJMlOK3p68/2NU69nrh6gvFz1XwXH8SHrjqoUMmk1s6dr4R+fxHzev2g4i0b56+brLWcf/TSxFwCIJFLcEL2NJouyizXBQPuL4/ix329xzBleLarH/kd+vXHk1xtHnq65laqqChXxJF6lxI7OKRbxcvnlcoMFACm8tLpLHGwSy5qi5qtoYoO1KW627koNgJZ9VpWtNC2QUYLKfurptNebP9SKj6UQSBsDv+llp0PYqErzEoZAJvY3t6lb7n9y+8dxiwEgyKoBxzhEEmHDoWmPrkU/vh59bv/j4oIKEploZctp2dZz56UFTh4GyxJocobQkyJRVWxpZnxFbja/NFdQli+o4MlEQplEKJNSUCKTRGESqUwixYlh7sGy9mTZeLJsHDVvKejPhR8metjV2uLs+/1BxfGVlx/XjO3haMkBgGXDOg9Ye7ikkm/Ootf/uf92kmIzD2+5buNoPm/jcEXhlBX9R87pDgAJ0el71l5MT8zjWrLGzOvZZ7SaGCwbKvsD5OQrDS+u59zBAQOANyVRrc2DAUCGY7HlcUda7al7+39kSmgSKiVClf21Ma5tx7jWfOjpvKJBj3awiLQ6txpJqUhgRqlujae3Ya0nKAHtPiS4u7q4X+NaU3GG0IkYk74pSnmUH/+iIDFXUK6pmkAmFsjEhVAJALFlWYpyOxq3o7V3Jxvv1hbuFM0OO1qw4jCHbzimUqhsv3JKKpysuPJjFEWcrblF5bz/Gaz8rJLfl52ZtLxf2x41KkkoAUUJ1ct8F/Y/Hjmne+tu/lmf8zcvPCE3WIrxl62TOQDY0FgAUCyqEepY4DVLfjAufMZCrzmKY7V9MLHB6m6jPjb1Qf4/RrQmwiR/xP8TaObR2y60fv3Si2JxFYekzRg9zPtkS+O0sXTXUkdPeFLxlPvn3+ZntbZxOtR9GJNUPR+Z4NNUPKoHei+/lvibadtMrMg7n/72RvZ7ntT4fbFcQdm59Dfn0t9QCaR+DgGjXdv4sA1zA1w3ode6Cep9KeTYm7NT80q8HCwBQCrDUvNKHCyrIzRC5+0gEQlvdprGVfDfhY2j+Z+3l2EY/ve2mw8vRZYVV9k5W3wzvcvwmdWbgN9M77xz9fmf5x7jWjBHz6te4VKMv+RYUBgAUCrmG9cHExus3rZj1JYbYbA+V+Vu+nhKisuGOamu2zUQmbwSJ4Y2nexrWdHj3cJMEmK9J+aVJ8fySI8R37+6syvm1aqQLvLyDW21/ZCMYPXkgz8fna5PTay2f6RUIpPVjpGSz/6M60ZEceqehAdRJenG3a4WoUzyT0bkPxmRgebO49zCdM7TAeDnMw/PP32vpYKfk/WpVeO+6dBi0+kHGyb1ZtEoO688D/V2ZFCrVToidi8k1DvXScNxLSaeQiR09/U0dGVKTxAEObvnXllh1ZbTc82t2RlJefs3XmGwqO37BACAX7Dr3tvL10zav3b/VDJFvW0xozAAoEzUNAyW/sx5u/NTRYbi1JJSE2MokIr+SPjnenb4AIc2sz0HavHTNyEFwgqeVOTG1Ljk/DAvji8Vj3Ztuzji1MO8T/LCcW5hmupr51Z6wtEeIxlE8oKA9pMfnJcbLJFMCgAogpBQky26a9G6UmFG980iUc1SoIUtZ1KHDcoVyBTi4cffG9qBxIq8rXG3wos0pkqrP9ElGdElGcfNXi5r1relmTYlidWju60eXRNkduPNp885xQuGqMoB9m/tV84Tzt55kS+SdG3p8f0Y411V1565e/lNtZQ+hUi89cNUSxZj+t4LB+cM136jESQVFC//5xYA2HPZk9sGT2gT1BBx35/epS//fSzbjAEAns0dxy/q/fxWTPs+Adu/O3v/4ltMhgHAYN8V8spjF/SiUGv9fuUjrDKlEVZ0Waxi7/5dWQwAyHCZJsmwr2aw9raqGVTLdwkVp5+rcokocXfoPF+2ATIm9eRDWRaVQHJhqCoFyhFh0t/ibpVL+KVi3vbQsfLCRRGnjH5cDq/SnsECACcWJ59fJS/0ObENAAgI8nniCqNblvP2SbxUUr0Rqcg9gRLQNl39UuJz87NKbBzN3X3tAGDbd+eWbhkJAIcer67nQ1UQY9IDSU8OJz+TanW0MRUxpZkTX+zvY99iuX8/S4qq04Yyo38+kZBVE7535O5bAPCws7jww0RF4diuQWO7mmB6XlBR9WbzfCqZCAA/nL5bKRBZshiShsnDeCaievCYU1bxy53H/Vv4WDBMv+7mF+RyfPvtkXO6m1myslILzuy+P3BiewBYvGXUwp9HIAii7KAX8SQ+JS5b+XYumQYAFUq7UjHlH+SBjT1tur4vqw5uG+k0RO3Tm+KiuxfLYW/oAlOJWwHAhYyIDTFXdNcDCLqxVn5wMGxqK4saka+tH28Fm7sWCivWvr+0t83E+itGWlDpRQKeHYNdKOBZ0aqVKqJGLwAAAoLElRRIcUwskxr9mHP7H0vEMgDwael89q/H8kISmdCmq9+yMXvtXSxy0ov/ebceAO5fipQbLLXgOJ6TVmSEr0MWv3RxxKnECiMD5Yzmdk5sRHHqluCRoUr/fCqcWa0aP4/j0HNVLUHOxKzCA7fCf5tRSwaHQiJ+FbFQfeCJxFfef1KcdvfxaAhrBQCj5vY4tu3WsuG7yoor7Vwsv5neJaxXtSdQ3RUDAhFV8dRlEilQe+9+oov6dSS1NEWDRSGQTGitAGC4c+hw55pl+2mvDvmw7Vb41yguDHi4fbRbm/Fu6nVIjqW8eFOccqbjnHKxYPSzvds/3V3iZ2RYtYIuDu4H4yJWh3Y9FBfR06k6w4r5lx3Dfo+OyDAcQZD+roYpsSj47aRG/xIBT7T78sK+3nr5ox765VrM6887Li8yyK/9ZWHyd1HnKgxRTTIhRaKqGa+PfuvdbbpXJ7X2Zc3R2zfefFIpdLOttXwplWHSOjJHV794luqPBZPeeuUu+TGFRFw4oL2hLejJ8fB3yql05nRq00APIhDQKSv6T1nRX5/KQe29g9rXSlgp318yek+8KRqsJsXptNf7Eh8dbTedRiDTaOTfQ8fMen2UAMgCv571aXZpUMeJ9875nNjawsL2SPdqXRSFS+KLYRplCxsNXqVwzw8XKsv4v5yYbZC1upvzYeW78zLN0Y4qmFMYweauzTj2nixrWxrHisKmEkhUAlGCYyKZpETEyxWUp/OKPpXnvC/NTK3SS4kFw7HdCffTeUXrW35T9+WXml9y95cZVhxt00YEMcDtWwubxvbZNLaP7nrqOBcZe/dTkj2HPbilX4izg5aalULR4VdRitOOnq7+9mp8/ZoCDKLcYFVvE38X86Pki5qeGJOQUdUFaxJC3hKwTnH6lQ2WQCa6kfPmfIbBmoSNAIZjmz/euJEV82ebid7saknfYHOX30NH/xRzZYpnvfYuLaj06wMnFwl4ljSG4vekNr2z0Ty88i49OU8hTYcg4O5r37m/tsxaO1efJ1OItk4W/Crh42vvBoxvN3BiB4O6dCs7ZnX0RS2x2QrMKYxBjkG97Jo349qrHQdREJSCEtkkmivTMszKQ16Ywy97lP/pSmZUgh6TzWtZ0QQEXddyiEr7bjbmvVYdqFt//6Lhrbyrl00tOYyo5Oygb7er1BndJfC7kV3r3KqePbdf/nVXNQnT98P0kmx9lJCy9lq1GOG5yNiOnq4/9OvqbM5VW/ngiwiFXhWKIEt7mCCrUAMh30NTeHpvCfhJcWlc+IyTbdT8uyjz1QxWYmXWzZw3Twpi2ln6T3DtcTT17tfqiSYyeCUP8z4dDJvix6nlet7R2ud296VEpL4beQiAYvUKAM73HWfaifD2Vee7Dw0mf5HZE4ulW1ec1W6wwno2Ly2syEkvCn8QJxKIuRYsg6xVRHHq93pYK3s6d6Znl4FOgUZ8hvZ07ji3sHFuYTGlmX8lPX5ekKi9/uXMKBRBfgwYoly4cXKfjZN1jHqsOMznv2tLrawPc/u0m9M7DEWQDecfjOkQ6PnFN/VmlGrASl3O1laDeZacNvDP48t6dhjfWnXvL6249PDLSMXp8ODmvrZNN76KgKAAIP6St+Fp4Uvl4OdHBaqZX4gosaNlzV78VzNYe5KuBXDdDrReYklhR5Zo3HqXW+LG8WxQwZVpqckw1d9a1cW0qgwAIJXKFm2qtXd+5/xbHX3o6ic/mLy8f8zr5N1rLty7+Hb1nok0usY8QApy+GVLI89onwmSUMI0z07TPTvX328jwMxpT+sJEcWpG2KvplUVaan5T0akF8tmrLEOKPVE+SUUk547fscZAHCy4Oq88VMdpReRVLrp1uPnyem/DeurrBuz/sZDyZfQVBaVsrCbNknorw4JRQEAw3H5AkiVtEqxiTzK6ZtKaZVKfaKhag3NOa3r30u0zi98R7BeyzTxFRnbE/75u+1y3VUbgIYwTP8WAtp67ri6eM3Ev36afmjzqW+1V8ZwfEXU2TKt7sv2dO4foWMN9UrXTqiF2/lOc3/7eOtc+hst1X7/dCfI3EVlpNz4BLjYxfy+GAAm7Tqrs3IpX/2WxZOk1FEHT+8bM8TFggsA5yJjX6bU+DOu6t25gTYHTYX0y8qg3JD3s1PvKf1dzI/Ks0UFug3WRFcTGIvF3lv1rElA0CqJUCgTUwlkCSZ9U5JgQzVBsGuTouulA9rlZRQodGZMDkpAXz+Iw3FtslY0OmXj37OSP2RpqqDgZOpL5XC/urTgOu5uPYFLNv1viYwSv28xsDnX8aeYy5rGdxJMtiLq7JmO38pXfBsfBEHUJasDANjx8GVsTh4ABDraz+tSE7iqrIA8OSy4sJJ340OC/DS1qHTM4TMHx39DI5F+vv1YUa2Lt/s3QTVRfg3KysjL2t9PmigQVgIAnViT53z+uxW7gn5VqZYlyMEBr7u42UhTQhuqvvOdIDPPC5nPBj79QYLJiAjBjma+2KeJ5s4kIijRqKmNFnkZFeqpM6O8iq9IjePmYwcA/Ue3Ob7zHoJAv9Ha9r9pDEqLNh7an5IrKNudoD4XnJzmXMd9bScxiVQDum4gg52CWCTK8shzmpxUM3gl7W5vbLgOaGfN8FoL7crZN/Y9C5f/y7z4nK5ssJRZ3L09hUj0tbX6/cFzeeUSnmDS3xesWQyhpPrNZ0anbRzUeOLRVzK0RTjppI2lq+K4RFyqto7aPagm4dYQYu4lF8MCACe61bG29fXz1g4JJahE+ZNQAgkx+KPYGlKdj4SIoGqzvWpCrbyMyWGwad8O/INErjZ5ErGMRicDwJ/XFgHAtz8OUal/8cBjmToPbAKRMGxGFwCY1WvLX3fVeG/tS3ykRd7Hhsre1Wp8g1orOd1sm/0QMOjH95ca+kH158CcYYpjxTaullxrFCIRAGZ0aOVkxln+z235ilWlUFT5RVuZgCLbhvezZDI0NmFqmpvZV4iN8LND2GRqSzPHOb6ddFdVt+HTJAxWI7OvzWSVkktd6hV839XWr6utX31akPM6L2NjxMOr/SeZZLtwx4V52WlFioEVgiC2jtom12QKUaYutlnhqZyRpEaSMINXci0rWlObKIL8FjLanNJIP6QhTsHx5Tmn01Q9Cf4z9PH3JhIIC89dV/ERW9itfTt358bsyfkuplypKBaXKM/+5PuGap1dTGawnOiepmpKwdTnJw530JGF8F/KqcRoCoHYx9mbQaqezBcL+Yuf35jkG2wq5wYHV0sHV/WhkWoZONEY551jKc+17AyOcW2rPRrZ5Cz26/2iMCmDV9KYD21Mevh6bBvWd+nFmwpxDQsmfWpYyNftVX1oa9Fqe+KfyiUIIF2t1fs5msxgzff6xVRNKXhZoDHEXySTUr5erqH6szb8niODs+b13YFufotatmeQyBPvneto7zq7eUNFVOhJTnpRcV65rbOFlR1XZ2WRTHIrW2PyKA6JNtenu6arDQSFQFrdfNDs8KON/NzGpI+/d15F1eY7T+SnxVX8nY9eLmnCzqLame85U//KpvzNizEhAJBR41crnuYni2W1ts/u59S42JEJxE421eO44KubPw5dY/SDvjpSDHv8zczY4rzDcRFdLx2wojHCbJ03hxkZw2ESeBWCbcvP5KQVOnnYpCXk2rtaLv99HJOjTdHwfl6clqxi493b1d2Vy00teHQ+PPx2dEFmcWUZn2PBtHe3Dusf3G1kW64VW207Wlg1eGtQ12YjF9VKxB1m5dHKwk1txsD/DJPDgpMKii6+q9bhOPDibWtXpw6eLl+3V42AKQ3W+o/TAWBjixNGt3Ag4YXyfn9Lc4cDiS8Up2S0xmDpH6rWlGlhYbu944CRXgHfvbxVJRFLMazh0k/oZN/6yy7eNmv/mgIAOI4f2HR177p/lm8fp+WWB7lxmi6RUeJoV9XR4s0jj/etPN1nYqdp60e4+TvRmNTKUl7Su7S7J56d3Hzl+2PfBnet2ZUX8kVDHb5Vzn5IppEX7ZjUbVSNC6hUKpNJ1GwUTPfq/N82WACwbkD3hPyiDzn5AIDjsOLSrcuzx1uztAVI/gcw7Qirvvkgj3eaZJKeKLO4HqpVjUOYrfPV/pOmP7w45cH5A92G0Ylfwa0fAMIffFTo8yEIMmZez6ldNgGAgCfCZJisjnQBDnhkSZqm1jrZ+LBr603npRftWXbiu4OzOg1tpSg0s2a37h3QunfA6d+ubZ7214m4beQvem9UOuVW6SHlFtaP2x0XnqxssDTRxtLdgW6WzVe/X/7fgEQgbB3Wd8i+E3LPhhKeYM3Ve/vHmSAXdyOzLvo6AKwLHKCzJvxbdglzBRV2NIPnC/8iuBTq3z1HjLtzdu6TKwe7DSM0hFKkLhhsWmlRpWIOWFpYyeLSAWDp8F0SsRRBQDn1AAAkVuRpcR3sYdtMpSTtY5ZMirXpoz6YsW2/wL83XirMLnHw0OjzQSITMf3STSOA9LUPOJj8RO1VOxrndvdl8mN+6SKx4CIAsKxuEUjNpaJnMkkchTkLACry22OyDAAAhMi1+8wv+47GXiniHSVRexJIAQBwJfPd2vfGJCswFa4WZit7d153vdoJ7mlS2p24pN66cs1rR4ZhCg1ok+hV6ORsaiT8lwzWvZxPW2Lv3eu9oIkqpxlFXZPEIJIPdR828PrfW989/S7YsOySJmH4jK6/LT4xZ9039q6Wmcn5e3+6PGpODwD489YytfU/luVoaa2ufp5bc0cCEY24H9t+oJotrTd3YrhWbGtHi7qXjKONpbsmg5UrKM8VlNvROABAN/uDbvaH4hIOMhyqFyVQghXb5oXSfTIcMAAZjusVpdA4jA4NuB4bH5Ferer58+3HHT1d6WTDBukkAkERjbjnyWsPq+p/hfi8GjGfBhKJN5QmYbA2xs3EcfwHfzXKErGlOeve3fyjzfAm8WmZjnejF9YttKDS93Qe/GfsaxmON/4gq//4dubWrLN/PpALKE9c2rdNN9VRkjIZPI1Jc+1oHCuqajZjG2fL+b9P3DLjQP8piR0Gh7o2c6TSKbwKflJ0+t0TzyPuxfxwYi5JQ+YCOWKRhIUi0U/iVg6qifQK7qK+k4HmzgQE1bTW+ak825YiKc+rJYJMIDWnspU9Y5tusgll1vbrNvSvk/LRUH5F1c5HL1f2NuyFx6VTCyur8279+US9Fxub2uCuv/qg22BlCVIEdUKotZBUGaPlKpVAr+uxVSFRs9aAA5xLjdz96ckvoYNbWf7Xtj/YZPVBbUFW9ge6fbVQpLBeLRRytzpJ52nUSHBlqvf/6jOpU8tOvg/Pvd6/+kx+RnFVOZ9tznDwsAnrFzRnyxiVXUKZFFvQZX3Kh0yF7yuZRl60c3Jg52a3yw/LS5b336KpD2SU6EA302RVkysLutk2o7JWAMgozFm4LJ9ftozO/U0mM2Ven8bB28ZyfOvAv19XC/gdD48e26qlJuUstfjbWT+u1LFHUU/Jmt2fHgPAPL8u9WkE9DFYu5NW6aPHpuBAygYtVxFAtrQ8p6XC1OcnyCjBgcF9nv/Zmso62nGiB8sA78f/0Wjkac6B6kDXmC3Nzs163HeDxn03SGf7Jflln2MzDr/bbO9ucD5qOS4MS00GK4tfAgBk+lB+6WJh1Q4EqGT6CJToIpOlCys2Cys2s21e1r2rIi8YAJiUxgvZ05M5ndpcePeBJxIDgAzD/nwavnmIASre41sHPUlK1RIbBAAT29QajWbwSvbGP83ilyn7IRFR1IHOneXTqe5vdk/8E2gcg4Vr/ztMzRj30HxBxbvizDxBBZVAKleKVzr5+a0IkwKAFJMRDInd+x8NAV+mUZbbzBSqDPKEUWSq8Xum5hSN3ZBrBqAER6bleUHFLyRqDyK5eu+Syl5JZaqX7mPbRol5R4zuT8PBpVMntw3e8+S1/PRaTPy3ndqoDLJ8bCyTC4sBwNNKdaGwg6fL0YnDz0XFphSW8MQ1YaEogrCpFE9ri28C/UNdamk0/xB17U1RWt2eRBVnZvHKTnWeaoK/Sh26DdZkt5UCmQFTQu3QCDr8RHra+wLAeI/WFRLhocSXk54d+zGw33DXIACILc2WCxUiCLK8RZN7yzURRj79U3elOqAI0pzr+GvwSDsaV89b+JrzCNAIZOXTawce7llmgHfe6qNzlF0fVNBzDQsA6LW7oUylRCis3Cqs3CE/FVVVf2hU1nIEUdzVGNnJevp5xmTL5WXqpRQ2OSz4+JtouVCyDMP2Pn3zy5BaUlNX5kzQcnsbN6c2bgYEUcWUZgGAB8uKQ65Z2yoXCz9XFn4qzzWs64ag22D5sYMb7vFaYJOoi/27hVm7zX111pLK6GLrvTl0iBHtbA8d203vyOQJz47O8O6gcE81uvKvH2+eTH1lQC+/NhiOx5Rm7k96rKImrAWRZoUGcm3VnYEzug2coSpknvgubUGX9Vfz/zJ0DKXnGhYAkDUL1QplEiprGZWlfgNUDo4Ly3Kqf8NEcghKdDeon3qya9RAk7TDolLGtmq572n1kvnVmE/zu7S152pzBvou6twY17YsEvX3T3f2tNZmzgBgccTpcW5hoRau8lOhTAoAV7rPVp7ryHCs+eUNQlkD7qI2iV1CLbS1ctsYPOjHdzce9vEyycZZYkXBvNdn7vZSL88glkklSq72p1Mj1kffUK7AIdPOd5nhxDCrW/k/QKkhkmxkAhE0mCyTfGUJRAIA8CsEVQxKVRm/srQqMzEvISIl4sGHiasHdx6mO+hSqHnSKo8wx6TpFQUqIXgInbuNTB8BACyrOwAYVCclJvDLltTjr2kMxrVqefDFW3l2MhmGnYv6sOiLXLLyG5RKIO1pPTHUwlWKY1JcJsVliq/x5cwoZX0eAoJuCPymv0NLAJBgsrpaYyorM42wUGOMwdoavwgAlvn+YdquaKKvY7NgCydTbfOLZVI91T4BYIxb6Bi3UOWSfvd2Z/PL5Abr/zl0AgWgUu0lLfJY+mNuw/Ft5TGr7Q84jpNpZHNrtpOPvU+w24r9M7yDXfVpQUvyOxqBBAAo0YVrn6lcLhE+lEmVUxb+m5ZKrViM/s19FOlU/3n3cX6XMLn/1Ar/fopEnMsjzxYIK9S2MMQpeIhTzYxqR/y9fM1bK18FYwxWgShbdyUTIVeYsaGpOvV8LQgo2si7EE0WBlHjClGhSP3vwSBQAvrH/e+hHtnPtOxj0jVoJSMICdH4oyAgQAAgIoZrPTYaY1sFKgxWQWXVk6SUbj6qgrEfy7Pv5n5Y9e48AIypE++pTFpV4WCnr7MipImm/gLRojDzVZBhmAlTB6oQW5bZ9d56TSK/TQ1bzcvzmSaVozL6A9eSTUfu5l4XIqWjPC6nLnTubwhqTmUtkcflNDShLg4WTLoFk97a1YB0Si0dbT2sanxKzkV+UKkQWZJmRma8H7Dh/YANPey0CcBXSYXvSzNb1YlY+LqY8l2RJ8yIKXtVJMpFENSKYh/ADbOmaEtXW5fHeYnSOuFLcoUZEkrobOuVXFGYzivBcVx+arKua+BRXuKSN+eVV2Q4ZJpjg80Hz6W/qpIKXxcld7DyaaBHaIdD0iYmo4IrQ2MYzefKArUZBJQhkggIiqANFvBRLKoqEqmfsQKAA72pT+pPTBlp3I1DA/233qvO7vcsOVUgltCUInX+/vxiuHOohltr8ffnF33tA75W2g5NmMZgCWWCS9kH3pXWSoJ4L+9ciHnnwQ5TKai+P4NDia9UFpgUCjNklNjZ1mvMk8PODHMiijacwZr3+iwAhFm7H24/Ia4st7eD/+aQIfpUrudzz6e/fpIfN9G907r35/e2me7Fsq1ngwaBIog/x2GWl745jQHARYM7OwCUSwQplYUeLG0On+7NnVTEGIyATCERSOoFeV4XfdZyoyeriWZyrz+DA/x2P34llEgDHGyHBflTSDW/8af5CXmCskFOQVpulxNdmnErO+ZMJ71y8TUmehmsTxWROODN2OoNs1Am2Pd5bY4gTX7KILIwHBPIeDjgESWPcwXpszx+ohL0slla5GV2f3oCAJUS0cVuDZL2quZBbUd1t/OVH+M4rj27hHJlo6mSCncn3LmVE705aGw7K28mkTr91b6Ffv0GO4Yase1yrtO3ps39p4kArdrHr4s+azdYJmHTPxp37p7mJ2i5MYDbqMLNjYkVi3FhxlgEQZTnhgCQKyj7+cO17aFjDyY9+TPxobxQ7RpWbFnWyqjz20JHmzx1yIb3N+vZgl4G62jqFgDQFFJzKXt/jiANAaSjVf8Olv25ZEsAKBbnPy289rrobrYg9Z+s/WNd1MT6aqHTze1P+y0GAEUY8O5PT+b5fQUNgwblU3n2tezIOznvXRiWR8JmuzNtAGCKRxdvtt2Wj1dPpDwb7BTazaa5k+bJV33IFqSdSP/jO127vYdSN7e37OPLCjyXuc+N4dvKvIu83J1pZUVlFQrVT7tuZceM+0rJlgGgTMx/mPdJ01Ununmj5cX4Knhaq35hkirzF709uar5AD+OvR/HfpZ3VwBYGnlGpZoMx06lvjqdFv5byCh/jmHrOfpwKkVH7nGd6GWwcNC4L5YtSH1X+hwARjsvCDKrcWmxINsMdZjuSHM/n7k3uux5Z+tBDjQDVu/kkROP85IWhp/f2mpoT3sT5KSRQ0BQCSbDcFyfXA8ogujvA2EEP7w/68KwWttiWGebWu7a7a18LnRafDUr8m7O+/Ci5D9bT6vng8olJZvjF/3S4phyoRSXSJWUUmLKX59K3y3FazwSZnus9WT6SzGJFJN8qV/LX6GtpYemlDmxZVkplQXuDT/IUss/mZFa/uE62nydJcKvyI/Rl35qObSu5o8Ku+LvFwgrTrSf1UAGXa1zksyQbff6rmG9LXkIAIHcDsrWSkEr826fKqI+lIe/LXno4KDXr25L7L3vWvSUH/+d/HpZ8x6Hk16b0GA5M81ZJKr/5fUq5WSUEDXoe5XC5mYOBxNf+F1SkzJ7d9tR9e/MhU61ZjQJFTmTX+590fsnFEHJKHG4c5vhzqbJSSHGhDrNc44gPdis40gn9Xtkaulj30JLjq9Dn59tChym6WrDUSUVHk1+pqVCF5v6zuL/dZzsOEv7HoicRX7qE8ebig9D1tYtVPv70kR9Ddbnqo8A0MZCY2RfO8s+H8rD5dX04UjSK4XB+lSW93vrYds/Pkyp1Lg/bSgMIvl2z/k4AIbjoMHkK+hk4xk1aDX2xfNKpfL+hOem6pUcMSaT4rKG8PKqklYwieo38pUxdMmsnZWXHY2Tq8Hd6VZ2zDTPTu7MesmSGMG+xMflEm05Pme+booBzA2K+hx/CEpEvloOAeOor8EqlxQBgANNY5iVI80DAMrExlicKqmITaLypeJRj+u7naQCostUKWOqRIHKnEt/9VvcNbWX2t5Wkw3ol8AxPez0VaqqS7Eo35pib/TtmkAR5Bvn0D0a8tTLcGxT7LVDYQ0VuK+WmNLMk6lqlGH+R122BI8EgM+VBSRUtx0goYSmYN3qa7CkmBQAiJr/YPklmVGqshwSrVjE45Bp4QOW+/6jOon7VzPSJWykS+OtSWcKUkrEhbrrGc5Yt7YnUl5qGtFEFKeeSQuvmz6ngSiXCFa9O4/9LxTBEDxY1jojnwFge+iYRuiMTurr6c4kcQCgSKRRUEJ+SV5NLSor+hM9a77cLc0dDya+bGFm+qGBJsgEoj5vGyMqf10+VUQVinLiK6ONu/1o2tZl70dFlaqZAjOJ1Mke2lJ4bo279aEsy7jnGoQEky2NOJ31n86U8z/q+3tzpnuViYtiyl7Z2jqrrSBflXeiVWuwPCm8ypPW2gjnSysAAP2yerI6oEYpca5fpzWR19YF1aTJ3N9+bD07rJ3jHSc3UGW1PMmPWxalUSiKgKDfOLde0Uy3OKd2EitjpLikl+3wK9lHPbx/JaEaYwA1Mdl1WXNOq1MZu9ReHecW9k9GZCZffTiOBJPNe3PiSLtpbg25mCXBZEsiT//ncxH+j/oarBactjFlr54WXg8y62RFUfVXzBKkvCi6BQAB3Orpz42c42rbMSfX8jzu5eAHAP5cu0vda6Wx1lOp6t9CZ5tmb/v+rOnqhYzwM2kvNF3VExzw23lnO1r27WTVP64i6mL2wdFO39azTRUoBNK6lkOnvzqsyf2lVMyb8frIntYTfdgN4r5fIREsizwTXtS0wk7/R0NQ3ylhADfMhuooxoT7P69L48UrX0qqjDmc8jOGYzZUR4XBIiJqNNW4ZMv+duOVS3a2GaFS54CxYysp9hViiU3ivUUlkOqf4Ppe/kWetLKjVT8CQpzksiSxMuZK9t9qLQsCqATTqMeinVALV+0LVYXCyikvDz7I05gp2mjiynPGPtv3P2vVRCChBAA49jn8amaM4r+jya/ARJtXeo2wCAhB0zImAshYl0W7k74vl5T8mfyDE93TnuaK4ViWICVXkAYAZJQy1nmhYld1rf8hCV4rQTQFpZFR3QGWHbWOrbT4mKRUNchis3ZSv8ZD6/Ku9PnD/MtzPdfL3xNcsuUs9zWHUrd0tOpnTladoLkzfI8WXo94/1S5cLCDvrm4l/n3ia/IfVeiMesMTypaEnF6kGPQ0mZ9uKYQfRfKJAeSnhz5/Kz+Zv0rIpJKS/nChmvfjE6lEFV/5hVCkVAi0ZmUFkWAQSYzKAasIdjR2Bm80l9ibte9ZEnRIY+uD3oZrJV+e7T8aXZUl5nua4+nb62QlGbykzP5yYpLHJL5RNfldjRXRQmVQKOCAZIAekImaPxDzqSF+3LsQsxdmSSKPr5z9QHDsRIx707Oh8hijb9btBHTZ7wsvjfWZb4TvcbpxIbquNJ3h9o+eLFabGrxNw44juMAOACCIAgCSFx5pD7PIiKEbSGjxz7fp0WFCgCuZr17mPdpmmfHka6tjQ5VE8oklzOjDiQ9LhLpzjaAAPJz0PB+Djo0YQpFpfMjt1RK1Wiu0gnUP4KXOdBUvfZNkvm5984jeRUmy5lQF1s28/GS6vBbgViy6fbj2x8Tq0QGDKWtWcxhQf7zurRVZISGapc9NVZhomfbn2Nu192opRCIM320bc7oiV4Gi0PSEcvmwvBe5rPjRdGtj+XhReI8BBArin1zTpt2ln30GT3VH5pm9e5SMW/h25ON0Ac9Yeqt11F/v765nmp8iLVbTAQQo/WnLCjMA22nTH11SFOAoZwqqXBH/L39SU/6OwT0cQgIMXfR04hjOB5Tlnk358PVrHeVEn1HJSub99dprQCgUFRKQknXO+1QeavhgM98u7FQVFbXYJmEBrVWKu3vfPzqQpSqQpZOCiqr9j4NZ1LI09rXyB/c6DFX7cLCOPfWAxxb5AsrlXMDUggkOxqbqvlHqj8m25WnEmjdbb7pbvN1koCam2K02TgwSfoarD72gX3sAxuyL3pBRElEVN+vmjPD4lDY1GmvDmu3WQAgkIkvZERcyIjgkGiB5i7NuQ6eLBsHOteKwqYRSRSUJMFlQpmkWFSVzS9NqSyILcuKKkkv1mNIpQABZJFfLz29wHAciCih7hgcAYSEEOE/4dv1KMH4lb4bHxKUDZYLU2PqSQ6ZxiGbfhYl59/hRqQTl4bRM2gIPJhfJx64LkSERNRD7Xea20ql+rotlwvD8mSH2Yvenowrz9GnG+USwZP8+Cf58bqrGgKVQPo5aHh3W41JwFRAEJBisrq6gzjgElwKDSYza8Nm5jfkIMuGXfMuz6vQ8RbRQnpJmQl6U2+MMVgEhKh2+voVaWmm3gusqYEiqD/X9KodxuFAc9WpLaPMSKfZeta0obKPtpv+U8yVG9nvjelZvXGkm20LGePLMUAXzILMFWHiAU/V6CAxiDQbisYBRT25u2CKQYvuXX4/oDhWLE5pwYxes1AoktZsXq/p27Wrj7vyspQKMgx7nJi64Wa1chZfbOQOsmkxxmB957tLi+DMV6GVhasWbaamQw+7ZmxDZIj/vVAIpJ+Dhnex9f059nqpmNdoz0URdLxb2Fyf7oaumNhQzc+105blsIGgEIm2bCMXNAy9UXleOzo0gEjQsXQ4OrSFwmA13JzYm23AnEO3wXpdHLkj6S89myMgqC3VZpH3LEeaaeJpXhS92Z18cJjjgOGO2hy+UQRd1XzAkojTJnloA8El0+f79PzavWhUetk1b2XhtjvhwaWMyEZwPggxd13m37cZp/Fiuf696LRWAKBl/GVCrnQ3QIjZxGtYMhzLFuQ+K3w9xlnj6ntCZfK6j7/WLbeiWOwM+kWlUIyJAUAoE9Wtr0J322Y/tRz6y4frJkmKZ3Kacx3XBgx2ZjTUzOLr8rE0b3H4P3f7qPGhNyMzfmgxaKJ7uz0JD+/lfsQaxmy1tfSY4dVFkZfYIM5k3DmedkN3PYARTj0nu5kmUfP/MA7dBqutRUhbi/16Nve08NXez0f4Mm1qRA3HEKfgzjY+93PjXhUmZ/JLikVVVRKhGJM18gQWAYRCIFIJJHMK04Vh4c227WTt7c91aGgvMJOwIvooAPwaOFmlvFBY1f/u/lJRjZsSiiBz/Dos8u8MABJMJpZpiyhwYVj+GjwyX1hxPv3NpYxIffyn9MGSwurr0GKgY2B9ZOxHO/ce7dxbtfDVqi0BC1wYjaGO//+cddHXAWBd4AB9Kpt4hMUk0gGArHUX3IflebqtvhbQUMzIjBEurUa4tFIurBK9LuXfdDJb/ym3p5/dPbU36lPn/wMvCtVLoefwy0koIWH494oAi99iH+6Je7Ynrlrb04GuWyDQhsqe59PjW+/u0aUZD/PinhUkakkdqAkUQX3ZtiEWbu2tvNpYuqEIuuLxnXPxNa52P3fqObZZy7XPHwzx8gu2sQeAkVfOvMnNAgAiim7v1m+gp++wy6cuDhk7+ebFPT0HMkhkALiR8zyhMm2Jz3hNzzWUp4XXZBpSTBIQQiergfuSf5ytzlHu/xtnUyPhaxkseQwdXV2OnDUffvlcpW8wvQvdcXOAGjVVZe5HJy0/dL19M7fdc4bUvZqYP8LNcieJYAcAOC7FcQkAYHj1dkxq0bel/OvyYwYlxMfmUt06/0MZHAcSgiqHg1FQ4gi3wJ9DBwBAdHH2otf6+nyjCBJs7hJs7rKsWd9yiSCmNPNTeU4WvySbX5YvKOfJRAKpRIRJSAiBRiTTCGQGkWJH4zgxLJwZ5q4MywAzJ5VkedmV5eETZtswaq1ASzFMIRZ+bvBo+cHBmIh8fhUAyMeDEplM9iU+RYpLJUoRoAQENSJlkTJEhISoi9W9lnN0sMNUAEjhmT6y8j+PiQ1WlbQKANgktZnlTTkvK+cLN5975OtoLZGqf4nJ8CoAjW7ibpZ/usGfACDDKuNyu5mwY00TDMdu50ZFlaQAAiHmHr1tgxozQkgLHBKto7V3R2vverajZ2Dt+4K8a8nxG18+ZpF1uO+ebLupnl1qZ9lHpWRj3Mw1zfZfyzna3rJvPRv/f4uJDVaFtAoAzEjcupc2Nl9twgdtvfgkzM/FjElLyFIfZiyVFRJRNd1QgSeOksjyozKcAcCSOc6EPWw6iDDJ8ndHIkuqE4veyom8mxu9JXASud7qg+dTo8+nRsuPNU0Jo/MXBNrsrFVSsCjQ+g8AKBG+rRQnuLCrZ2EYLv5Q9H2A1W/17JWCEZdPIwAxU+fLzVOJgB+Vn5M0czEJJQy8qCpzhAAiMUoXVwul4sJMfrIT3dOMbAUAFZL/iQvWFxMbrEJREQBYUzXmBJbzsSLhccHzxMrPpZJyALAgm/mwPLtbd/JiadSGV+bVp/TnH1Mv/zD50N03aitIZHkSWaEM51UJ7qcUzQbNxqio8oSb5R4z+sBK4fNSvl5bRf86/ky8FVny2YftMMerLwHQP5NuvilO/Cv59nzvAQBQLK4Uy3T/UBEEJDimnB5NjElHuQdtDOkPWqeEGKhu2uI1dgGPL/45vrhGEcyWYczQA0EQqbrNx/NDxrSyrXHT3RX1eoRPc7n+SV08mE7H0q73f7pA7VVPptOO4OUG9Sq5KvZy1iE/Tsit3JPfOM70YlXHMy71+d2gdpomOfzy/YnP4spyy8VCHHAUQYgIgUogMogUBpHMIFFYRIoFlWlJYVpSGVZUli2NbUVl1X/XycQGK19YiABiQ9XoCSbDsYMpxx8X1tKlyxMW5AkLnhS+7GXTdbLbaO27aXyRZP3p+8uGdeYwNMb6VwpfAOBl/NuWzDHBzhmajBFP/E4oTebS/8vj8wJh+T9Zr5hE2ragqWZkJgBsDZo6+uVv5zNejHHpZElhD3n6sz6uBtZUllAq8blQM1Eiouj64H5abtFEIf/JndRmbe3PEhEGAKK8VkAmmBnRoLeZRdjxWq6CU1oEq9S5lZL4JDNtZZtOrvu2AkDdKaE/x/1Ce5MN7gDgVu6pkc5znelefuyQ69l/KwyWDfW/kHT6u8hLEUUaJUnUQkYJrkyLluaO64OMdw0xscHK5Gfb0WwomkV4L2fffFz4gklkDLTvHWwWYEE2RwApFpdElr6/kXvvbv4jC4rZIHvVyb8yO68+d7Ux699KS6ZCLK9ir5P5xoLKAxaMYQiivjMSWWFq0Tw3i10fsttLZLnwH50S3suLxnCsj12w3FoBAJfM6Gcfejb92d286LEunUY7dxBhNYOgi5mv1LZjR2dHDlmm6SlkAoFMUD9ywetYQyt65yCb3fLj3m765n/Twtr23X5o300++lO861Y/rdnqPRcfu/3ty9ODRrlyuGmzlwFA3SmhyckTZtrTXAHAme6VL8qWF67/OJ2AEL9vtk/TXTIM54lEDAq5cZw2jSamJAsA2lq52dLYFAIRRVAZhkkwmQSX3ciMxQFcmRZFwqoqaY0HpRiTJVYUFIt4pjdYRaKShdGrjfPxq5Lyxryu0TWe7Dq6t23NqvbtvAdEhLjOf4UDrcbDxYFm50Cza2UetPz9jzdy72kxWO9Tcq6Fx51fpS3JR07ZVgrR2Yo5USLNyy7b4mj2Q906Qsnn1KLZjtw1DEpwC4dwAPivTgmjS1MAINCsVsrfIDP3s+nP3pV8HuvSaa53f+VLmgyWAhmOnU+Nvpwem1FVWiERWlGZLc3tx3u2Uus1CgBkAvdOak0EcojtfgQhAkB0/oJ8/n21t3RzeUnSY/1RmSOxUeZU2hCvmtcYEUWJCAoApULB4dgoubUyqE2daBHeYJGoXJJFsSjPhupUJMpVyCWu9T8IAK+L70lxicq+B4bjm+88OR/1QSCWoAjibmXe3N6mrZtTBw8XS2ZNHmYKkSgPCawry9eYiDEZABxsP6FuurxXBSnFIt65LtNZJKpIJi0UVRUJqzJ5pRm8ksTyfCeGMYNoBY36Nwtkwiopz5Fmr2ytFNhRbSwpFvnCQqFMRCWo+SqIpbJ1J+/N6Rdmb8HW9AieKKpK9NbD6ggA2HGWJBeO54tV3+E4Lk0rnu9kvolJaV2/P+hfQBqvAAB82bUirr1Z9gCQyiswosGl4ZfTq0oX+HcKMLdnkaj5gooHOUmznp/9PrDnN64t69ZvYbWludUvAKDY47ekdQAA5ZX49wVLW1j9jCLGS6dJMZmKFvb6Dt3lB2ZU2u0R+uqmGkQ7K89QC9d3JRkqUUdcMn2qR0cbeoszGbtDzDtHljzpZVsrT7hQxpPh2GjnWutlp9++P/b6nfwYw/HkguLkguLL0XEIAiHODoMC/Aa28KWRSccmD88qqwAAR67GX0GjoTa5J5tELRbxKiUiFolKIRAd6VxHOjfQ3NEkT1RvsCwp5ifbaBy1yln6fm2OIG9/6O8sor4RmDQClUlk5ArzM/nZTnRV0YJsQW6hqJhJZKi1VgCw/9ZrOpU0tkuQlkcwKMHeNuflxwhC9LQ+iQChUlgrPxWCEH1tb+rZ5yYIjUhCEVTT+JdRW8azRFwJAGxSLUliDokOACUig2PFE8sL72THhw9awiZVP8WJYTbZq7Uby2Jp+GW1BgsAf5zRUSyr2SCzZw5qYbUZAFLLDyaWVK9A5/FuAYAja7i/pTEJKBFAZI0uWUUjkA+FTdNSwZJil8FPGuY0y/FLpmEMx1AE7WI9pG7la7HqBXZwHCLSsyPSs7feezYqNGBau9CWjk3a/55KJAEAT6o7nM4IGntU2du268Ws6+s+/jrAvncr80D5GlaRuCSyJPpG7j0Mx3rbdlV74+fc4uMPo44vG4OiBmw1IJpdsf69EBHCdM9Ox1NeCmS1FD8oKLGvQ4AjvdaQW4LJAIBSW7pAnk5RosEPWwsIAiggxDo+XBSUoOlfBccxDiUw2GZP9Slg0fnVIws3znQ3znTluu/y5xvaJTkubO6cu1e+e3xHubC1naPCZVQFKoEIACQCgWDI18lQnOleznQvxelIp2+1uL+lFOlweqgQig48f3smImZmh1ZTwkL0iV7+Ksjlf/nSBpGjaWyDNdRhQK4g/2Xx23OZl89lXla52sGyzVAH9R76yblFIol05C9q1koD5213suReWzdF00MRhIggJABAEY0bi/rUaTrM9ek+16e7PjXpREqlRMCTCjmkmnUQvlQEAAy9xZoVeLGtejr4THl6ar5/p+ZmdgwiuVBY9SQ3+Y+PT1a1VC9EgSBoiTBceRnLgaUpMB5B9BAUVEsvN8/Ps5bqX//8kDEAcLTfMOMeZxyh5upfxnKqhDVDkpaOti0d7RLyi2Ky8wTiWn4hlULRtvvPr8XG/zy4V3N7mzrNfH3kwj6ChtEgaGyDRUDQ+V4z2lm2flH0Jqnqc5GoBADYJJYPy7ObdcdAbnNNN/YO9ukd7KNS+PulpwlZhX/N1/G1Y1LaMiltAUBLkKA+dVSQ4rJsfmkmrySTX5rNLykWVZWK+WVifpmYL5RJ5DsmEkyKAEJGiWSUQEIJLBLNjEw3IzPMKQx7upkLw8KJbu7MsDCJ3LVa7KhmlRJBgbBc2WAVisrll4xocFubIedTo3fHPUuvKqkQC61orJbm9nvajQi11LRbj/RwidCzcblD6X8DLbGEcuQRhYpT5cQNe0YPki+0i6Wy16mZV97H3f2ULFEKL0/MLxp96MzSHh2mhIU0QN/rhXyEpY9znxF8nY2GELOWIWYtAWDym/kYyP4K2fZVumEEGbziiOK0uPLsuLKcxMo8iV5JD3GBTCyQAQAUidSEU6II6s60as518Oc6BJm7eLFM+dr0YTsmVubElqV7sWpUoj6UZwCAN9sY7VMCgo52Dx7truro9D9U0BRLqICgOcOIYluQTCR08nLt5OVaWMn76/mbsxGxCrMllWFb7jz9kJP/y+DeZGITWvqQLyZqSWRVH3Q3+lvCnqhSjUK3MyOWKJ8SENSOarPIe7bafUCTQ0DRBl2DkCPGpC8Kkp4VJL4qSs7hl5m8fQzHkivzkyvzL2dGAYAlhRVm5dHB2ruzjQ+NYHBaeRXaWnpfy37zqij+G6cwRaFckqGdpS8AdH+4pmkqiP3bqRtLqGDdhynrmh8xqDUrFmNN367jWweuu/7gdWqmovxGbEJeedVf44YwDcke2KDIl1YbaNJgegG/LEHu86LwUU5D9KksPxBhojJxRamkrFhUWiAqzBXk5wjzsgW5q30Xa29h4WATZDrTQkRx2vXs6Pu5H/VPKlV/ikSV17Kir2VFUwmkzja+/RwCOll7Gx2r3MGqmTWV86oo4XVRQltLHwB4U5z4ovCTLdWsg1UzAAiz9BUpGayXRSZOBvH/nHJJcXzFOwDwYQdxv6TL48uMlANztTA7MnH40deR2+4/l8qqfz6RGdnTjl88OP4bFrUxUurpRIphUJ24sJpHeYnFQtMooOk2WMt95urf3KOC5/tTjlVJVTW8N8b9nlj1Ga0eIeMyHJPhNbp6k9+o3xsql1bo/2gTIsVlt7Njj6W8TKjI/SodkCOUSe7kxN7JibWjcUe7tvnGOcQIPXgiQljqO2Rl9LHvov/uatMCAXhUEIsgsNxvqPwrtTGglgJU+3vfmewPqAOGY5s/3riaGa2yv/kvhYgQaESSOZnpzDBvxnXobOPjz6k1y87gJ55M/yPYrBOGY3uSVo93WeLCUF2HNRQEgSlhIS3sbRecu1bCq1bKfJ+VN+f0lcMThjWFuaE8rlN5K/nbVybTLjfxCItCoACAOoVPXPIl/gMBBEVQEkokIAQiQiShJBqBSifSOCS2GYljTjazpFjYUKzsaDZMIuNRwfM6TTUgOOC3s2N3xN/LFZQ15nO1kyso2/7pzv6kxxPc2010b2/o7l4Hq2Zrmo/8I+HqvbxoAGCT6It9BslHW43Mp/Lcs2nq49X/jUhxWaVEVikRpvOKnhUk/pX4KMjcZX3Loc5fks5dzzk+1HGGLysIAFwY3tdzj831rK9qjZxQF4fTU0dPOX4xp6z6pR6Rnr3i0u3tw/s3WEIyfZGL4imHFpFQgn6rvbpppEX3Nc2WyieAKII0WaXgT+U5G2OvfSjL+todUQ9PKtqX+Ohs2ptvfbqNcGll0MfYxy64s3Xzz1W5AODJtKPWe2nMOP4bAystvCtJn/jiwMXO8ywoTADIEaR6MKr9ObyYLU6l/2HCZ7lYcE9NHTXu8NnsLzbr9sdEXxvL2Z30ShzbcIhkUgBQVsW433thlUSjH2n/+3v0b7zxfM/kEo5N01phOHYg6cn45/ubrLVSUCrmbYq9NvXloQxesUE30gjk5hyX5hyXr2Wt/p9QKuZti7stP+aQLIrEefLjIlEul6xDdslQbNnMIxOHKUca7nz06lVKhmmfYijyPRyyksGyprLcWZaa/jOo8f9I5uf6UCbmL4k4HVmSZtzttjSON9vWmW5hT+fa07hmFAaHROOQ6BQCkYgSSAgBA0wsk0lwWblYUC7hl4iqcgRlWbzSdF5RfEWucbkUo0rShz/dsy5gSD+HAOO6/T8ajjs5H1b49+OS6V2th5xJ39XLdhQO+N28s92tTe+n6mzO3Ttm8ISj54QSKQBgOL7y8p1rcyawaTr8nyuEIrauRfoKoTHhNfIRVv3lIdXy/91gZfJLvg0/ZtBoBUWQFlzHUAu3EAvXAK4Ti6Tjm4ECgUgkAACHRANQTfNVJubHlGZGlKRFFKfGleXon+BHJJOsenc+viJ3kW8vPQWC9eFQGyODY/6HAikue5KfMNgpKNS8K4dkEVv+GgAGO0zxYGr0i64PLRxsNgzsufyfW/LT/Iqq9TcfbR2mRuWNgKIyrHpvcd6Zq61dHbVnfn6bnq10r77fMbmQA1mDUGI9MfWiO0pW/N8kkFEyAKgNhy4TCHc/fb2md5dXqRlZ5RUjAg3+NmTzSye/OFikXwwwAkiohesAx8DONj5mZIbuG/SDS6Z3svHpZOMDAEWiqif58beyYyKK0/S0XH9/fl4iqlrf8htT2Sxftmmi6tVCNzwY6F9KQkUuQBAAeLECFNJ9CoY6TFd3k/EMDPB9k5Z5PuqD/PR6bPyQls06eLqoVDOj04qqqnfw36RlvUkzYAGEQdH3306+vv7VHEcNIsSspWlTeLW3bN3eUo0IjN+mP+QOtcfeVityfH/93vah/fr767v5VSyqmvX6qD7WikIgDXcOHe3atqHToFpSmMOcQ4c5h2bzSy9kRJxLe1Ml1e3/dS0rmoCg61oOaZrrg8r4ceymenY6mxbeQKH8TYeUKvWpBuSEWaqmQaw/3/ftGp6WlVFSJj9df/PhtW8nqGhmNbe3fpyob+YqFTws9f3yS3EZ1HZrMCH/1inhpDbBh19HKpc4cNj6WysMx5dFnsnkl2ivhiLIcOdWs727yjd9Gg0HutlC357TPTudTnt9KPmpzsD3y5lRTgzz6Z6dG6d7RoMAstC350LfmjDpH9/ePZYQGWLleKG3NlHGeiLDscBzf1RJRHObt1sWWN9PCQecLxXHV+SufnchT1Cutk6FuLFzCVNJxJ8H95pw9Jw8JDGjpOzoq6hZHWu97Ke1C33xOUOiNeWtWhAEJofpG4wldxwl/iumhI3Gyh6dVvboBADLLt/aMqi3oXqyB5OfRJXoUKR2ZVr+HDjcn6st4G79q4cimWxTh56FfF6bk3vfTZzHodRa0sqsLL+Y+PFRxufMyopKsciSTvfkWvR18x7k4cck65g4M4iU6Z6dhziF/Pbx5u2cWO2V9yQ8aMF1amNZk8WjWFQpxnQEoJJRogWFBQDlEr5CwkEhnvVL3AUAWNVsuPItXa/8lVZZMtEn5KdWvdS2KZJJg8//wZdK1oR0n+ZX84MJPLe9XCxUKSwR8gGgVGTYz3vV61tX0z46Mbm3B+g1t5JimHxb3dAHqQUBhEGkhJi7DnEK3pf4SG0d/tdw4Ah1cfgm0P/iu2rFygPP344IbmHOqHE2buXqeGXO+Oux8SlFpXyxGNOlIIYAQiUR7Tisfv4+gU6GBds10Gj/X2mwbn9KWnDxuuL06ofqaJIunm77Rw/ReXtaVZGm75mCbrZ+GwOH6XTRlGKYXOhSimMYjqtoyN1OTVz48EYnJ9d5wWGBVnYcCrVIwAvPzfr7Y9SuqFfH+o3wMrPQ2VtLCnNL8MiO1t4bY69p8WPCcPz76AvXui5ShB8OebZJ5zcSRZBnPTb/FHv6bl60orCPXfAPzUcBwPXst1DHYJWLBfDF0KhFJJPypRIAKKttHcrFwrqFxpHLr+BLJXl8YzZYTYgtVWOya2WZ/MZkaY+O9z4ly3f3qkTivU/Dv+/bRbmCu6X5gq7t6v8gLaGCvhzb+reviX+lwerj55W4pjrMcNE/N38f2tegJeddCfdlWuXq+zu03Bj4jSJ8L6m0eOS106XCWr+0ByOneXC1zepFMumKJ7dntWy1JLQm4NGeyR7q1WyQp9/CB9eWP7l1eYi+idEHOAa6Mi3nvjleJtZoKQqFlQeTns737SE/HeHUXufPhoKSAOBxwQcA6GMXjAByKzfyUX6s3GA1WXgSMWiVVG8ctAR4Nrr6aTXmDNqsjq1/u/dMfnouMnZmh1ZWLJPtESl43k9jRpJL3WaZ/HEK/pUGK7GweMBfx+THNBLRd9Mf8uOpbUPk80QtJFTk3c/VlqmlnZXnxsBhyhYwuaxYhmEpM5YZZBbzeFUVYlE/dzXLagQE6ePmvfiRYTLNzbmOh8KmTnpxUMtK/PGUF2Pc2lpSmACwwEff3CTymaPcSN3KjfxaowP9KRbxAYBDbhCpxfSqBy5MvcQRmybj2wSeeBOdW14JACKp9NDLiJW9VZftIvKyj8W9e5ubVSTgc6nUQGu7Cc0COzm6qWtPlVl3L09tEdLG7uskK6sxWKmZRTkF6lcQmw721hw3J0svK4tXi2clFBS9TM0Y4O/zIjWDTaWEONnbc3TL8l/NfKflqg2VvTl4pIphwnGcgKKG+g3YMVhsMuVeWrKvuZXKJQzH76d/bmapMXujJjxZNr+HjpkdflTTXE+ESa9mvZvq0dHQlv9FiGTSjMpSAHBjN8im7dPc7yd4/ZsM1tW9d8VCyfDF1dmPKETizA6tfrrxUH56NjL2205tlP1Ir3+O3xT+eFlohzVtu5pTaRVi4cvsjB+e35/aImSSf/XK+vWUhHn3rypuIaLo9q79B3r4AoAEk0kwY/JpmYQagzVj1UmBsKm/WmlU0v0TC58kp2598Kyjh+vkNsE2LKavjdXNuMQfbz6wYDD++EZbak8Mx27nxGipsLRZH47highqIRMIv3TqvejhjYTSolE+LVpY2TJJ5DKR4G1e9pHYyMzK8qN9h+tupQ5tLN3HuoadSH2pqcKljEidBmv1++Ml4spdIbM0pUFuyiSVF8nXCn24qm8CQ5HhYr40n060JSANpfhqQi7vvr132XEcq35XUeiUv6I227vbSCUyqaTWxt+woOZ7n74pqKwCAIFYciYydmaHVoqr2yNf7Oo2MPRLTmxzKn2Ah29La7u+F48qDNYAd58BM2syXe+KevW5TMeWeuNQY7CavrWCL53s4unWxbPW8LVfM+9+zbx13p5QkVck0qjL48227W3fov6dVNDf3cff0vqfxI+/vX2WVVleIRZZUOmeZhaDPf2GeDVjkIx0r53v2+N6drSmxawMXnEGr0TuMrYt/jIALPUdolInT1iaUJGdXJXr15Buog3Eq7zq7d16Tglz+W+e5a0VycqoBLOOtutt6a1039OQIEj1ypemoXxuasHKv+d2HVW9ZP7jN9tKcsvs3dXo05KJhEltgxQrWSffRE9rF6rsql73EdqnD0QUlTWM5DEAeLMNmGr8K9ewjOZjeY6Wq984GyOPfTY+9my8Rp8DV7bZktAOyuvu9YdKIA13bnUw+YmmCrFlmXKD9U/mK1BnsAK5bgkV2Z8rjTdY19M/XU//ZNy99eRuVqL84F5W0kQfIxXNhbLSJ7mr/c3GOTI6ZvNePM5dNcT1PJVQrxyf9WRN327pJaUA4GahsRtEEkHtcV1GhLTY/eS1PIFFCU9QIRSa0aunDotD2i98eGNZqw7t7V3MqLQKsSg8N/PXN09XtNK4/ivFsAZzVIAr3efID/qbTb1Relh75RqD1S3MJzldm3tuU8DTpV6zgIRybYJ8Ha2NkYga5dtiS6c+ubzKsJPVmRyPfXy39oX6nMZq2d1j0AB1a/Na6Gnnr8VgxZXl9HeoSRGYUpWnUoFFogFAOr+p/3PXJV9Q9a6wOsDtZV5aSkWxO1u3a0hdUitv29FbtTCfAgBmFM9ScXJKxe1mZmPkV29mTFaujCDEvk4H69VvPRjXWm1WRyNhUykT2gQdD383LMh/SliIwloBwAAPXwcW+8iHqM3hT4oFAg6FGmRjt6FDzw4ONaE8z7LSpt35R/zFxZSIon907a/6jHqDyTBcaTVWKpbKpLXmtgiCoLWzmdUYrA1LjU94/29By3yQS6arZPQzmon+QRP9tWV7rT/ebFsmkappu7BYXOvPnPBqu9pqBcIyozswwMVvV8chai9ViIUtz6l/IgDs/vBy9weNC3A6ORr/Vr6ARUAQGY7v+fBqWzv1eeG0k1n11N+sxqfEgz3gQ8lRhcFyZdVKWYYam3zs67Kke/t5nduq1SANsrYP6mZft1xBSnnJeL/Ate26NVjvAACmBX0nFtS4Flram030q5UjgkQlHY3dqlzSpP8lPI5uPddvTIi1w+20xHXhD16PmqP/vXMeXmGTKVs61EoEUCnR6LVoR+NqukRAUQkmk2IY0UB/+oYDRRA7GiepUr3BKq+9vDXetYtKhTIJ73r222LDMz9/XXgS8amkaADw4VoNcPHb9v7ppZTYsV6BIVYGT2wrJJkWVD/FqQXFt0JSk9mhmdk4U/T366NirTa+fnQwRkfKtW7O7of7DIPakqENxJGY3wy9pUkbLCmGyeOexHV2Umc+uGRNY25spz55p/xeaR3vUJHmUBUtKjEBlrZsMtXzoGousqV6rEzFlxSei48Nz8tMrygTSCQsMsWVYxZm7zzaN8CFzdV5uxa4ZLqmSypZcOZ4qSqN5AlLr2e/LRZ/BYM1r3m7pUrRfPOfXdZ/LWx7zLMKsRAAFgZ06ObgeTHlQ1plyYpXN6/1m0wnGraDIZKVk9EaJxgyyhTJGtWnZ83GS8MHhwa20ObNlJZRvHX3nd2/jlWUKO8GquwM6vXQtl3XtK2VzLXF0Z0RE76l1FFWQACRYDK+VFImFJQKhRmVZe/ycx5mpPwQpi0XbCPwlQ3W0bioH1/XWu6hEUk7Og/o7eKl6RY5EgwTG64SzdD8tdaS99GOyXo5dhYOIMMwHHAcB7lbFgKgfa1qb3T4zqhX45q1/KldDx9zSxqRVCEWfSzKv5z8qdf5I7927jPY00/L7drRYnyZuiS6GAQqAJSJVXOFNGUSygqPxkcAgA/Xqo+zLwLwW1j/0fdOplQUf/fq5s6OQwxaEyajLAnGpxCqbZYUF5BR08e3R71PX7z6rOKUSEQ3/zi8VbArAEilmOxL2ptN227cfVjjzMxmUQ/tmmJtxZJKZcprOlaOFpsn7vllwm75KZVBmb2toSLGPbjmm14/PvohikwgWNOZHlzzIGu7bV36Bljb/f0xyoQPurT7TnFuqVeQW5u+gVSG7tCFr2ywJjcLntysJgo8raK084UDogbbQGURNfpYlUk0hrzIQQAMmhIWCXi/vnn6V68hvVxrjK85ldbR0bWjo6u3mcXqZ3cHevgarWNVrHk9TqcrGY1IBgChushECkoSYZJf4i7IV0MbSDfSUPhS8YLnl2U4RkDQzW37yT+yUGvHH0K7r3t773r6J1s66/sQA7w9mSTbMnGyDa36u1cqSmGSaoJ7i4W1Bn0oQjSj6HiDqiW4pcuTGyvkx1U80YyFf3u6q9k1+n5p/++XVi9pi8TSoeN2EwhqvhXDF/dXeIc2NO0dXBKm6UiyZxL+WnVqyd7p0U/iDq45M2xB38FzeiJafxFN4uuo4HFWKhFFOzvoFSJgBDY0ja7wGbwSvlRs6MxCJ5rCzYgGe87XolTM05LSVWU97nVxgtpqikQmykJa7a38HubHyMOeAUCeu1AZeb8LhTy5+ajbbDavOieCqRZBcIBlL28klhUBwPwW7QMta1aLJ/mEfiotOJv8/uCnN1IcWxvaU88P1YHRIb3qkcJgZVQ9dGC0V1y9mTlFuTIC6Hgv4zcKAEAokvy0+erE0e3MuDrC+s5fimgV7GZhXj3ci0vI7dz/V0tz5sXj39anA00WHMN7je/Ya3zHssKKrTP3x75IWHX0Wy0eG03LYF1Jieto76qi0GJCfNkaJTIwHHtXmt7eyrAXKRFFNX2GljTGslYdFz68PtE/qKeLp5eZBZ1ErhAJ40sKryZ/upQct6VTH6ON1svCZC2SpM1rS+IsjVLv24IDjuE4iiDHw2repRsCxs0XDhDIxAgAnUixpKiaeHsGp1jID8/P8Dy5RXsnHRkaxQz0B8Px78Nv38qIB4DW1k7zWqgqDWxo3btYyL+flXQ0PiKjsmxruwFmFN2xCm6s3tfSx9jQAh0Y7XN4rxPLLw10OaW4OtbzqXLleiojpqYXbf7jFo7jV268IxCQtqEebJaabziG42cuvrn/JG7H5jGKwmY+dnt/1zHvI5IImMzIWBmS4TFnJJRAaoD1eK4Ve925xetGbv9j7qFl+2dqqtaEDFZ0YW5UQc6pPibTCbiQ9OFC0gcAWBLcYWFgOwBoplXc6nJmlKEGa21Y9b4vEUFRBCHU/refG9S2o6PruYTY1c/uZlaWC6USJpniwua2sXO6NWyyG8d4L4pTqa+1XG3BrbWUu8R3cN06v8dfAQAZjqEIwY1Zy1vaWrNqCgCsDOr6ffjtzKpSmWZFAgaJHGbjMsBVdXRmKCKZdOXrW5dTPwBACwu7Q11H1B3TkVDC3k7fLHpx9Ub6p4fZyb2vH1gR2GWYR4D2XyGL5BBqtfhZ3loclyEIoa3VdyxSzXeDgJhmoJ2WUXzu0tuXbz5/t6hPWCuP8IiUs5fevo1M+35ZrZmdRCJ78iLh9IU3nu7We7aOZ9ANe/qgOeqFyfQhauI8Q2/5q9cQox+nHSKJ8MPJBdNDVr69G9Oql/rsKk3FYGE4vvbVfTsGq729qhC10Qz3ar6tY63QQnemlSPdLItfqrb+w9xP6bwiF4YxuZis6IyUGWoENwKsbAOsTCwP9DDvk5Z0ZEHmLuaUWvOOYU5q9I/kBgv0znmhoJ2ty6PBDagfoiCpvGjB8yvxpQUA4Gtmfaz7KE16MkQU3dFhsDmFdjwxqlDAW/n6Zgc7N1s6S3v73pyh9vQ2ZeLPXLKn8gKWCdm09XqrYLfjf01jMakA0CbUvU2ou9zOE4ko4YtL5OzFxz3drVcv7e/hVmuFi0isTl/y3+CXqX89uRgOAEQycflf0zt/0xoA/Fp7Kteh0Mkzfx6zb8WJVr1+VdtIUzFY+z+8iS3OIyDoifjo8b6BivJRt87IDyxptX6EJBSViyIZSh/7AE0+4lJc9suHG/vaTDKi2UajVMzbGHtVS4UhTvpK2YJWRSeTwyFTy8VCjh7zNTkTH5yRS/T1cvLe2m4AS6v6FQFB1rfu3dbGZX3Evf4ufjqtlRwmyZ5J0uY/WU/275ikJmoPAQDYuGaoouTgrslqp2Wuzha7lHwa/u18jqkOApWKpWkfs+QG649Ha1WqdRzaqkUHjYEfTcJgXfkct/ntk/VhPYgo4YdX97gU6gA3X/mls31Ht7Vzvpry6afwh8q3DHDzXf3yrsthNWaYQSLHTVik6VmDnAIPf36GaRDwe1WYfDj56VRPHaJaXwuhTDL/zQkt+4NmZEZv+5rsQUvqRBEq6GcfigCoXTVvIKJHqtl1kq83qV11Wtyy4+rXt1cEdZ7ZrK2ej+jn4tvLybue+YNaW2mUpjMUeU9W/Hhh4uiw5n61liNWrf9n5JDQoABnebXDJ57b2XD69mxRWSVc+v25/TsmAsDKdRfHDG/dsvnXUZ5qZOa2X7vnxXr5MddK4+aYDoPFF4iz88skUhmbSbPgMmhU06twXEz+uOL5rdkBbSb6BQNAqVAw79HVtIqyeS21fU0HufsNcveT4bjc9BBRdfvA6nBhWPZ3CLiWFa2pwq6E+zY0jnIsXhOhSipc9PZUrNbc1PN8utOUEjsPcwrTVPN7/xGm7JyxrG/de31r9SlkRnq07Ovsq31gVZf6RyP4cI2R/dECjuFzl52sWz58UE3kNobhMgyvPviygp6VU9qgyqUVxVWnfrsW9fBDQWaJSCDG6/ewvpM6L9xp2OxE/kS5H0NydJqiXBFjqC2WUAWJRLZl3917zz9JlTYgWAyqm5OFm5NFc2/7wGZO9jb12gaS4fjPbx4djotcEtRhXmD1T2tuy7YsMmVvzOspzXRPbQgIQkAMnuTP8e52J+eDpgQNGI6vib5YIRGMcdX3xd4IZPCKF0ecTq7M11LHm22rj+DElEE7Dl2ej375Yb948Gn9sup599yV/QeNag0AV8++kYilwyaYQPy7PhhqrZosu38d28K/VvzQynUXVer8tvP2bztvA4CnmzUARMdmlpTxLt94p90bvj78NG7Xx1dJpmotI0GbFIpaxvssJpGJFBrZzLrGkswIXsmvEsrtFJVGPhC1WfkWjQbr74uvbz1RlRKu5Alj4rNj4rOv3IsBADtrTuc2Xp3beAX4att900R6RenllE9Heg7r4uiuXD7RL2iMT4A+2nLBp3b/2KbbYA81u1FEFNWUGc2Bbjbft8e2uNuamsVwfPOHG+9LM9e0GMgkNpSPhZ5gOH4xI2Jb3G0tGSgAgEYgbwkaUXdN6s3zpFeP4xeuqYlsF4ukyq/S9t397rz7SeUumVQmleoVSHDu8/vvXhmm9awMhUD05VpvbTfAk2OM6ILRfCw9juEG+CejCNHfzHi3cmkdtwNJnY932fzeA/u0LC3jLVtzPje/fMsft35bP+LcpbcHjz2bOqGDCZN7y8lNLTChtTKOouySO7xjAFBeVDnSZa68MCMh53bV35rcRzUarCdvdP8xuQXlZ65FnLkW4WRnNrB7iwHdW3BYBsh1unPMw0fNUTuG11MJU4zJxBrUWvd2U7OXr2CCe7vnBYnhRSla6tzKjnlTlLLQt9dAx0CTf1305FXh5+2fbidUqOrD1OWHgEHuLDVCaKVFlWSyxn/l/q3X1w1J++3AFLWVGwKRTPq+OGdHzDNN2g8qTH984UFWUqCl/aU+9dobyeW/aTSDZW3NXrTqjEohhUw0n1GjhWBrw9m2687WXXcAoENbrx9/ubJwdg9/X/vVS/tv233n0rWoYV/mj9n8D+fSlwDARPf9FhRX47oEANmftY3WjcDZx/jtC45lrU0SLc7uGr/KOfk1saAIAgggWnJGZeaW/nni6eHzrwb1aDFmUCtrC732aMAUKw7GgQDya/CoiS8OpPOKtFQrFlWtff/PoeSnUz079nUIoDRWnIoYkz7IjTuW8iJOq+Kggnk+PTQtuuVkltg6cDXdeOON6h6NnM8J2oTDlBnp0XKkh5HrfRJM1uf6oZSK4pQKfeV3y0UCMDy9IBFFmSRKlUSkWN3v4bDLoBbqw/L5vZfP15HqeUDvgAG91XgeUcjE1UsaJBxHyK81YN/99EfPlqoeReOfHY4qzkAAedJnqSWVeST5pRmZPsQ5cNCDP692/xYAMBzfm/Dkn/R3VVJRihU1T1BuS+MAwMmUN2JMOsXTsCWFQz+c01lH4y9QKKqJ+P9zw5jmPvZFJVW5BeWfM4oSUvJj47PTs1W/ZEKR5NyNqMt3Y0YNCJnwTRsGzcRhLqaFS6b/1XbShBf7C4U6RAvSeUU/vr+0Ne5WH/sWfewDgsydG2hzTYrL3pVk3MmJvZPzoUKzEo4KM726zPDSmM04/Flixx7aHDjDnyb+/eeDrLRiM0tmv2Eho6Y0XgILEkpgkykAIGyw6FE5BASNHbVEd73/T6gssasdH53oOFX5VKGAogi8P57yOqY0+1jHKVwy7WpmzNzXZ851mUFAUBmOSQ1PVOHqr1smSK8hQ3MfexRBrC1Y1hasln7VjRaWVL2KSnnwIj7qYyaG1fzxYon0+KXw6w9jF03t1qO9r6GdrtU5FNWe8JqIoPWJlLajcY+2m/Ft+DHt4yw5lRLh+fS359Pfskm0tlYeweauQWbOXmybehovCSZLrMh7V5oeVZz+uugzTyrS/14UQZc16zPOTeNWYExEGq9K9Pj2hyFj2zKYNYtx/UJ/IpIIN96sraoU7v3t1g9bR7l4WJUWVe3cdM3R2bJ9d+M1JP6N4IAJpMV8aT4OOJNoRyNaNJwc8L+Ib1+fepxXrUZtQWE+7qPG4t/L+fRL8BAHOhcAxri1upfz6WNZToCZkbrb3Ue3A4Bfp+3TUkcvg6V2BcfKnDmoR8CgHgFFJVVX7sdcvR9TVFLjH1Razv9x+/W7zz59N7uXha6AT018nlztEaMpfKmrk/vaV/e+f3lXUwtrWned0VxbcgFHutmx9jMWRZx6pytzvYIKieBuzoe7OR/kHXNjWnmyrB3oZrY0ri2VbUZmsEg0NolKRokklEBEUQzHJZhMgskqJcJyCb9UzM8VlOUKyjJ5JUmV+elVxVLcYJ0cAGCTaL8Gjwyz8tRUQcAX79x0bfrCnmWlvF0/31j58zDFpZsRP8rdrDEMA8AViVhwHHQmi/4vIZJVxJYcTqm8LZKVKQqpBHMPdv8W5pNIDSA48y/iz7bVPqu3sz++LUqTv5jXvru69t1VG2qNn5RyhBaGYxTNGaFNggkWZSzNmdNGtpv4TZsbDz8c+yc8v6hCcelFxOdJS/9eu6Bf65au9XlEX1fvvq5qkuJs79R/e6f6zvC5ZPqhsKn7kx4fSHqiPSN0XeTjo0Q9FsVNSycbn7UtBltRNa4VCnii1XOP2zqYde7dHMfxN88S9/9+Z8biXirLmWwOfcbi3r+svpCdXswxo/ceHNzh/83wSiAtup01g0IwC7SYaUsLpROtcMD50sJcfnhyxbWMqsd9nP6iEhok9eG/iDOpb3/7cO9mj+qQw/VBg4a7BPe+t1N+2sfef1PMzbUtB5hR6OfTIsvFAk+WAVkX3PzVeGyoOF6pYLJVZBKRMKRXy/5dm5++FvH3xdeKJbDScv6SjRemjWw/ZYTGmYv+zHh+Zrxnq862HqsirodaOg1z1b3cG1uauzT80t0+2tQ5CAg6x7tbJ2ufDbFXP+m3zv21sKayF/r2HOAYqL1aUnwuiUz8/teRAIAgyKrNI1bN/js7o8TRRdV7oH03v/bd/OaN/WvDrnFmFv+PxhQRRTtsaaFhNquVJ4AcsguH7OLDHf4yb0NE4Y4OtqoOHw3H9awNSZXP9K9/LEWjpIEyXLL9FI+jRvQnvDB1T/xjLpm+pmW/6S+P/xr6Td06Y91b82XiKS/+rpQIW1u6/hk2Vj4QIyCoPvtp+95sqlt4rfiQlltMvO1FIhEmftOmT+dmm/feCf/iuorjcPDsi/Ts4tVz+5BJej3xWd7nqc9OK065ZNrJLhO9OVZimUwuDSqWSVU0Qoc9OBxTUm1rfLk213rOkB/LMEyR/EM7/lyH0x1n38h6vyvhfp6gySXBZhKpUz07jncL02fUHRDi+uv+yTX3sqg7j89E0FrDq2vn3uzbehtBEKlUhmP46B7VAttMNm38TI2r+P8ZcnivB7meUbtchQAabDn3avqYupf+n1AkrFobffVb3y6DnAIQQMgoYfenx6GWLnLfRsV2OYogM707zvRW3agZ597a0CcOmFGtv6g9fVmD7NNbW7B+XzP88t33O448Ekuqzcq95/FFJbxfVw2l67F72NHWI2nEGvmxDMd73vozi1fmzdE22rzYfaqWq3qCADLAMbC3fYsb2e9PpLxM0upZ3mg40M3GuLb9xjmEQTTe81vFWgHAwJGt+w8PRRCkrtvLpZOvjH5Qg0IjkgCgUiISyqTUOkrkBoEDhoJG048iZByMWVs0mgGOP6iUXM1a97nypZ5DpFNp8/MFCTQCZ7b3ufpvGlhSmbd7LlAIgfV3bNHPsbniVO7ToGD447/iymr5wRAQ9LfQYX0c/PV/4vw/9HKsa0DHoiG9Wvp7263+7YrCpetdXObC9ee3rxnO1EO8WQGKIMUinitL42rCwHsH4svUWBZPtuWt3rMN7TYAkFDCEKfgIU7Bb4tTr2W9u58bZ9DmnamgEEidbXwGOLTsaO1jQs9VEpmoaAzVMG4nEAlErCmuvntyLJ/lppYI+X6nDU64IkeRoMya1jKu7GSQhfpUTB9Lj1nTAo3tpmlwZ7b9XPmyTJxTIclnk9RkeFYgwngFgiQAsKf7m2qLEwGkUiJse2NLXanINpZuRzrU2JcCYeWHwT8qf0UPJb3QJOJUTxrWE9LL1frg5vHfbb4U+yXOKC4pd/GGCzvXjdQ/jvpDaS6dSHJnaQzdUMz+TE4rC7dWFm7ftxj0LD/hWUHiq8LkfGGF7tvqhzmF0dbSo4O1d1cbP5NLNgPA0WsLddaRRxQayvG0bTQCY7iTAS8JLpkGAFy9NWZnNGv9sSQvpjjXaNct7MvPL8Ry/u3MmWWiz+7svlbUACqBCwACWUmh4H1K5a0i4cfejvuNe4SpcGO2BkAA8NSqNy3NtKUNzaiKwgEDACe6KYP2WSTqxyE/qhTKcKzHnT+USwigKltKQgna/Y1Ob71+eut1tZesnSz+fL6OqcG1oMFdtzks2s51I9f+fv3Z22R5SVxy7sotl7auHkbSOllVcCE1eqBzc+11MBzfn/Dyn7T3ufwKaxproHPzuX4dLqRGr426BQAO9HoFaVNQYg87/x52/gCQUlkQWZL+sTw7riznc2WBcR4JKiCAuDItm3Hs/bkOIeauPhzbemryNhzXc449LbymXPKd7y4LSrVCIQYymYHTqCPdRhpU347OPttrvO56esAhu/V3/ju6+K+X+RulWI2bLhGlOTO79nc+xiDqUF7UJCgIAMx6TN4VMIjmNlSvfGFicuUL7QYrufKF/MCNacybxiDqOh7KAJNrbStKJJjM6ClBQWZxaUHFVzNYAEAmETcuG7T292tPwqvjEyNiM37+8/aPC3V7JKRWllzL+HCjV43E5bcvz8sPQi1r9kR3xT19W5ixK2y4G8sim1e2JebBxui7PwX3HeMREl2cvej1P6b6W9xZ1u4s6xHQCgDEmDSLV5LFL83kl2TzS4tFVWVifqmYXy7hC2USue+VFJcBICSUQEYJZJTIJFLNKHRzMsOczLCnmzkxzJ3pFs4Mi4YYSTUEA+wnDrCf+LV7YTKYJPsOtj9huIQnzeNLiwCATrRkEO30TPXczsoz1ML1XUmGijcMl0w3laSaB6tdvjAxkxddJS1mEtVPMqSYKKXqFQCYkR25ZGNkCAyFXHv10ILMaH6l1nYqAUG3tTKxSo+cRgqOIxLQDUsGrvr1yovIz/KSu88+uTtbThjaRstdlRLR3Jfnv/XraEevcVT7s92Ing4+S8MvK9d8VZA2x6+DD8caANxYFvOadVz0+tLpz5EmGWFpgowS5farIRpvUghkvHUfpmhJe0Enstb5q0910fRBERKL5MQiGaziQiOQD4VNa4guKWjO7fO66ASGS+PK7ra2VL9rmVT5TIwJAMCH3aVBO6PgTs8FyqeXuhmQkl0nlvZmZpp1qxpPcZRAQNcvGbDgp3MfE6s3FPafeu7jbqPJp1Qglcx7ecGTbTnNR7cuVTtrt/3xL21pLDeWRTavfFfc0/Y2bmM8Qkw+wjItf6fMn+TeGFG4Yqls6Y7LEZ8ylXVOUBTxc7XZsfgbDlPHEhKNwNjSUndg6v9QoZ4fOwAwiOZerPYJFU/el14LsRhBUDf0iy6Vy/ODH8eAzIxflzHLBkz6QY1jl04aVSyBSiH9unKojWX1cAnD8Q27bpaWq8lgmskrHfHwCJtM3dZGr4y+85p17GjrPv/VheDLv017dtqXY7MmsDqVCJlAIBOaqJJ/vjBZ+fR96a0GelBUfNbrD+kqqkwYhn9MyXsUaYAokgQT38s79/OnOatiRv8Wv+BV8V0tw67/MAO9l+tTTcvH/jAiUc9ntTQbDABV0qKPZXfqXs3iv88TJACAMyOoceaDX5fG1nTnsum/rBg8+/vTcv+skjL+pj23t65WtbU/Rt3u5eA7r1lHPbfzUQSZ7dt+tm/7upeacW21u7k3GgeTp4swPgIIGaVP9zxQt8Kd3J0tzfoqTk/cjjx49RVPUEsGxJLLWDu1d1gLV4MezRNqdMuoEujrsYEDfiR1sxgTTXJdbkWxz+QnX8zany/MHOLQsNOiRkbtx27FZf4wrVcbP2e5eZZKZDIZhnyJI8Fk1T4gSO3IEi0fO0+obwoVB3pza6pngTD5ZeFRH3ZnCkE5GgF/ml/9RQoyH6r29ibOwYhIqTq/biKBMD00BAB6H/n7zpQaF4qvkITCx91m0dRuv/5VHbH8Kirl5qMP/brW2gc82GG0oZ5Hx5Pfrn+n5hWEAPRw8Pmz3deXMC8RZy9vdhMB+DWun+7aAHV/NgBQVMY7eSfSUINlEuLK36bx4r9vto9BZAOAB7P5WOeFO5NWtrXoaUt1lteJLHkcWfIYAHrZjuph0yDLrg2N2o+9sKzq1J3IA/NOikVSALCw5UzqsIFEJh558v2UThtFQoncTlEopEOPV5u8S+2sJl3O/EEgK39ZdKyrTc3bN678fr4wEQCsqB7uTG3LwY0PjuF1fZXrQiEQ5Nk8Nz56vKZrF0W5IrInubhYuf7XyZozuGfAi8jPLyKqF+B3H3vSLsSdy6YrKhjhJznBs9UETzXCDBFFGcveaMuL1ZgY5K9Q92ejvbyhKRLlWVBs5dZKjgPdHUUIhaIchcEKMe8yymmuphaSMgtX772RkVdqQk0IFEV8nK23LhhkbaavbKR2NH28VXzxkcff1y3Pyyy5mbJNi0hm/XFjtnGit8zkv39fcrUZu7sNzQcAhLKqFwVH5BXaWU021F9UpcMyY3NHA0BVOb8ktyw/oyg/oyg3rTD7c35WUl5uasHgWT1m/jxa+70TggLlBxsfPZ4SojuNw1dL87VqTu+xCw9XVAkBoLxSsPvYkzXz+uq8i0wgkA0MyCAiBGjCkinXs38FAATQ/g4myy7VQFhR7YtFeTxphcJmZfE/Y7jMhqLv/tr5h+/TcvUVF9UTDMM/peXffPlpcv9qFyS+4JJUmslmVe9kSaTJFZXbLcz2AEBZ+frKqr1fbkUtzHfTaUOVK+jPh7cpu74/v+/OCtAq6WsqutrOO5E6B8Ol17M3jnPbQyWwbudsqZIWAYALI9SI4RWZUstzO/xWdMchrQBAJpHKZJhUIpNKZDKpTCQQ8yoEvHJ+9f/L+bwKQXlRZUl+eUl+eWl+eUl+mUSk3kc08qFqUgidFPJ4x99FL+7QXtMH+tUMlhmHPmd8py37qieGt598HNk/xNtNh4vAgQ7VBptMIOppuUgoQU+F+K9CALcPmCKh6cQfTxz7yTQelZrwY4e4M5sdSd0y1HGaFcUhk590MWt/R6v+1lR913qr+A0V4aTcskxWhCBKG3C4FL7490plGbbWD0kkPwDg8U7KZHkqFbTzx8qz9i6W/ca2ozEpBzddHTKlUyOYKjkWFJfWFqNfF52okOTfzP7Fnt4stSocAIgopZutwenmAcDSwUz5dPO0vzZP+8s0ff1CdnKeVCLTHsys4NSokSKpdOnNW35WVlo+0/r+TmKefdqz+G8AmBXynaJwdujK3tRxKv9N8l2kcu/A7gHNvauFWXEcdh59pHx17q8XFMffbqk+nrOl2mt0uXe35/+kdJ61a/Cyg9eefVSppoy/me29vk1i0V0tzowAZ0aAI12HK78KylOq8iohAMSnFchPxRKpIuDctCCATHJd4cVqcTR1y48fJl3I+qujZf8B9k0uUbZMlkkkaB70IV9GFvp5hypz52x4ZRl/Tp/fNn37t4e/Q98xJlBM0p82luPkk8F0XuSrwuPyws7Ws7lkY7I/uPg6aMlXahIs7MxwvSNSrRiMcefO+1pZreyiTSlE478ZlUKSa1rVDfpb1mPDyr/nWjqYA4BMIpNJZAAgEtRowO+L2KxyC47jk3wXy6QyArHG3CIIzJ/cZdbqU/LTdx8zi0qqLM2rN0FkSprQikTNitxT+y+97NPOb8uCQeWVgp8O3g7ycXC05kqkDSsNXn+KRZma3skG+Qd0mr7z+aHqkMBe8/4MP1otX5uWWzL/14sIAjuXDXO1N734HAkl97Yd3dtWx8LE10Uoekoiasx1Xk+mrRrYMszzp5mHt54zZlxTH1CEMNDhh5OpcwWyajUBP073ADMjBSwJRHTC6iG7Fh8zQccIKNeKZWlnZulgbu1kYedqZedm7eRta+tipXPd/ZuTp+gkMglFPxUW/tqndyc3V3k5TyyW4ThWx95pNFgn/5hSXMoDACtzVVE3fqVAuypgXRAEIRBRZWslp7m3fec2Xk/Ck2ws2ctn9rBUepZYIms9+XfFqfzY0ZqraFB+IMUwqQxrtJF5fWjO7Xk3dxcANON0VS7/K2myFBcDgJPe4yyxUlY75QW6v6+/mTq4DYlAOHr9zbqZferfZxUkmHhN7PhZnuvcGWoSW6BAIMBXnn2LxZGA41W8YwzGaFDbGUV2L0PSfCkT2sVv5prBvy0+tefmUkrjZlqplBYqspMhgASZDalPa/2ndrGw49449CgpOr2ipAr7su6OElASmUimksg0Mp1JZbBpDDaNzqLROTQGm85g05gcOtuCybVicyxZZlZsrhVbnw1BtewdPCi7ouJVRmZaWdn+txGeFhb2bBYAjDx9ViyTIoD08PBQrq/RYNlasW01jBhL88tZZobJtOM4TtQg3Td/Uhc/D9vh/YJVhnJkEuHNl4HD7M3n9q0cCQAzNlXnd5s1tN2mI3d/OXqPw6RNG9zWwapBgm9MSz979YlbZnkdNdUj3iVkzxgSRiSgf/3z0lRtKoMDhgMu05A0e4LrUu236+PbbRwcJg0AcFxSWr6Gw/lBJHpVUbmLzVqkUo1IsMsrULwtUAvzP4173IAJ7R9dibp+4uWwGV2Gz+yq+wZTkFz5/FbOFilWvVqHA34+Y3lvu+VebOMTHbXtG9i2b2B1g19efY35+rdhMm2YzGB7+1mtW+148WrUmTMXxo6xYTJvTFKfBdLgaXxRTmlpfrmAJwq/+W7DmB0A0H+67oCA4twyK0f1MxQ7a86Eb9TsccgTIsj9G+ruuXJZtN8WaEuVqkxyVtHjyOTEzMK03JLyKoFAJBGKJY25c2jJZWyc1S/EV3VhJcjY8bwmCkurrM1ZKIIUlfNM27JJGNk9MDI+Kz23xLRuDb4uNv3a+QHgJWWLUdSMRu1BpbTLL+hLJDjT6bV8krmcDVzOBqOfNXZ+dfgEgiDjF/fZ8d3ZYTO6TFulTUfBJOCAvSo8Hl50CgAHgNYWoy2orndztkow4fXsDaHCEWFWk4hIfcd6X3eaQkTRpR3bM8ikaRcvXZs0wWS7hNGPPuA4/uLK275Tut4Rnnz38MOzf94oru5acOT6/vua7u1NHecZ5LrnlRoh57pwmNS2U7bLj5t72MkP5ArLR66F7734QqX+xP5qnLAy80s3HrkXlZClzxMbjqIy3q1Xn1QM1rZPA5f6XdN0i3GgKIJhGKCq+kQmax8hAIAUl+isqRY3e4tzmxpqkV4my5NKU60sTgEAgtAtLA6Ulq1UMVjyaqVlqywtjtQUIURA9JrJTlhSM8sO7uC963pj5DqslBTeyvklm/8BAAgIqafdEnnMIJNocT1ro0BWHlF8PrnyRTfb+S6MkEboT4Myu03rPt5eWr67hhksHMPPbbs+b8fkf3bc6jG2A4miuh4/f+eU+TtNk+j81/mD6hbuWTEcAKYMbDNpQGsUQTYevjupf2snG678qmLCKKe4nDdz87misiYx1hBJZDhgytkrZbgUq72bjiAIUr99W3srTk5RBZGA2lo0yAYQESF5sQKOpqpRoVQwwH5iJ6sGH3TUhUCwtbG6oTglEb2tLdUEveMgAag1pSURPQ11wpLDNnBhxHDwT+UPHuf/KZRVAQCX7NDXfqUtrXo/wZHecpzbnls5m7P5H8rEOf9krPJhdw6zmmRGNjItYEPjEeCSlZQHAEQSwUlzXvsF129cnaDRQccwg/X3T+ft3KwHzupZnFN6ZO25mVvGGXS7oVx48N6CS+8a4qUomfXLub9WjYQvrvAogqAo8uFz7tQNp0FpSV7O3n9eNBFrJedg8gwJVuMuxCRa7EuqNdwgoZQZntpShiiTml1MJBKk0lomr12A68O3SSQioV2AqxE9rChbyecdAwAC0dXK5iUASKWfeZXbOWa7FXXGWlsQ7MZRadXzcak0mVf5h3KFJg9itFj7xQOPZVJt9xKIhGEzuhjXuArZ/A9P8vfJI28AkCDzwR2sphHRWrqALJL1CJdtUSX/vCw4IsXFCRVPEiueerE7trYYY0X1UNvsV2TV4VmrDs/SdBXD8T9fh88La/sxv9pBhy+REBCEQqxlowwwWJ/eJH94kbDh0nIAGL9m2PcDtyR/yYujwrrh215dj1IptLQ3O5li2Ncaw/HvdtWaNCmbpPlbL+5aNgwAHKw48uV5hZcWAPBFkjuv4w16XEOjxRg9zj/UxcaAEOKhXQJ+PfYQBxwBZGiXAEX5xH6tlv5xBQB+W6hmfKoTNnczm1vbJQWX4LV30ySiCCJTKbkALsWN3W5rJGrP+AgoVyyOysy2q12J4GifiiA6ZLvJFKJM6/44gWgC+ZN8QcKb4jMKEVELims323mO9AC1lRFAQsyHuTFbP8jdkcWPwQFPrHiaWPHUkd7Sj9PNi9WxdrB0vUj/lP05JiP9U3Z6fE5+RrGgSsCvEgqqhDgOdBaVwaIxOHQHT1s3fwfXZo5+rTw4lvoGS73LybVnsy7HfZoXVq0llVpaOu7seQRBDg8b6mNpqahpgMHya+259X51Yg8iifDLjZUoAX338EPdmvkZRWcy/jSzrtm5w3F8rNs8FT8sffj52wE9WtekUJ2+sWbSJxKr/k72flcT4fwpNU9Yp0JTYGfCyAU+5wBAiotxHCehFAB4U3zBIIO1akoPteVmbPrhtcYkp5LJsgvzVBcBOWa/k0iByiVi0XOxOBKrKCVbhiH1XuVtHFRmfAjCdLAz8k02cGIHE3VKDUJZVVLl07iyuzmCOHkJi2QdZjWxGaenzhBUc7LTCJetqVVvnhccLhKlAEAW/30W//3DvN2BZoM72eiVwVATH18lPbsS8erGu/yMIk11KoqrKoqrACD5ffqTiwAACIo0a+3Ztm9g5+GtrR01JmSQM+XiP9v69Q12qJkn/vk6fFnHDiiC7At/s71/jViA8aE5BrliGbcBwWFSv997fbXS1nOAZ82fRCYRlB215Nz4Y6YVlwkASZmFRjyx4aCQCDxpKYNoJpRVAkA67/2tnN8BoKfdXA9T6HAP61av7AMEgoOtg5oMslJJzW9bwD9TWf6LueUZAf9iSdEwDvc3Ism3Pg/9z/Dk2rtOAwLrucuWzY+9n/uH/NiM7NjSbGCA2QCCrkGfMm7M1q7MVgnlj6JKL+ULEgBAhktsaMb70EY9+nhy89WPrw1QTFOAY/jH10kfXycd3XCx26iwUYv7O3qp0chvt2//y9kzK0WiE9HRg/1qso5HZOcs7dAeQZCtz54r169vLCGBRCCQCABAodV8spb25qOdVaNhrBwtDB1e9W7r27utxp/E7uXa1Eua1OqVBYfRN8xvT+K4Fc1uykueFRwd5LgKAG7n/FEfg9V+2o4XhxYCwHeT6q82Kass3yjg/4PjPBI5hMPdUlYyQyL5SKUNAgCpNJFXudfM8gSR5MfirOHzjpSXLbewugYIETE8xuU/A47h+zddKSmo6DwwqJ5NebDCfDndMFwWwO3vxAg0rhEEEF9ON19Ot0JhSmzZjSJRmg/bGHX50oKK7fMOv7kTY1w3lJFJsXsnX9w//XLI7B5T1g4j13a1za+qkh+IZbLObq47Xr5SlFsxGIAgBbxaP+T6ftUCOvoFdPQDgL8itygKN1zWS4+xQdEikGbFZW5bONjDwULPNNSm4kFczc5aoSjVhuoJAEWijJNpOvwt64JhuHzDUSKVyWQYgiAoWu2wRiCgABD5KTPEzzCRcon4vVgcaWF9C0W5vKq/eFUHLKzvSSXxVZV/AACR6G1p80gRfEpnTKEzpgAAkej5r1pxNyXZaYW711ywtOUu326a3ae+9it11qmSCns9XD/do8dUj25aqp1K/3Q6LQeA/Ht8tT4Xh0S/0001V6taMuJz1gzbXpBVrFJOIKI+Ie4t2vs4+9g5eNpa2ptR6WQqg4pjuKBKyKvg56QWZiXmJsekRz/+VJhdS5YDx/BLf96LfZH409mFFnbcug91NzPjiSUO7OrdbQQAw3GkjtLUf/bdqGUBq7W/s5+rtrSUjQAFZQpkFQA4jcAa4LDir6TJBt0+YuUR+R9oZcYctPQgmUi4tHXa0GWHBGIJjUy6tHXanM3n3/xtmJcQidySTA4qLuiF43wSOZTD/bW4oKdihAUAAKhEElNcoBLxgzBY81jsVQY969/LzB6bCUSCjYMZhuPRL5K+/embPqN15xzQkyJR5fHUJ88L4wuEZVQC2Z/jNNy5bQcrP+U6UkyG4bhYQ7CBgoU+/Rf6GOOWXJRTumrI1uLcMuVCN3+nQbO6dRnWhqYhVoFEYbItmHZu1iHdqjdkMuJzbh9/dv/Ui4qSKkW15Pfp3w389fe7q9lfgvD8rKrTuXtbWt5NTpoaEvLd7TsA4Mhh51VVAYDChMn5zxosLRpYbEZDBYhox4/TRXHcgtvzQd5eHKA5tyeHZENAiDhg+jthXfx1at3C7MLy8KNL2tRZ19MbAovzE4tTk6/JwvqeVJrIq9ypKCGRAlSWuiSSD7zKWiOs1XtvPIxIUo5dbwSIBJRKJpmx6Y5WHG9nq1bNnFv5OaPGBrhpYde1JaVFVUW5pZHPEnJSi+6cC/cLdnXx1pHBUB8SK3MXRBzyZNr+1GKkD9u+Uip8kBf7Y8zZPvZBy/30DeqoJziOb5mxX9la0Vm0WT+P7j3R4OgfZ1/7mZtGTVg95PimS5f33VcEKmYl5f0288CGC4vlp9e/hOD4WFkefBsxNaTa97W9i8vtxCQCinRwcVZu9r9rsDRDaIDvsT4MdKhR4OlgPTGu/CEANOPIB/YmSi9ev2YwrKS8dL6ZxUlFCZHorTzjw2T5hfkdcbxK6SaEwZqvOKngCe+9SahXJ4xCKsOqBKIqgSgzv/TVh7S/b761NmPNGNx2cKcWpvX5p9DItk7mtk7mzVt7jFvY++rR59+N+fOnw9N9WjrrvlkzEky2KvqkK8NqR+hUeaZSczJzhHOYL9th1pu/PJg23zjpGMc9zP+wOvqk9jrK2FA5VzqrzkCfX42MfV7zz8e2YG67vcrJW8UFxABoDMrMn0eH9myxbswu8Rcp17f3Yl9ei2o3sEZfdEarUFczs5i8fEXJ7DatZ1++guOwb0gtB50ag5WaWZRTUG50zxoHe2uOm5Ol7npNmG+c1gEAAoi/UlKmYPNBBvm47zjzRCyRdQx0b93cxZRROLgUx7Vp7MmwIgq1G9d8n6YKEq1+lY1JQWnlpqP3wj+mb5rdvyGGWgBAJBK+md7Z1dfuj+/O7r1dr3Xbx/kfsvnFq/2/Ucmr3ILr3MHK90jKo6FObbQ7N3Szaf669y/16QMA3Dj0WPn0x5Pz62OtFAR39f/h+Ny1I/5QRHpc2ntX2WCt7NwJB9jSp5eixJrB+Gfc2LpN1RisGatOCoRGxog1GjQq6f6JhV+7F/XCk9WmbhRhV5vpBjVy6nbkmmm9rzz58OeF55u+7e9kY6b7Hj1AULZUEp+XrRI2QbC1T5Hr3hFQS5HwgUoFAsHeyjbCJB0wOfffJvq4WCvUkxuC4A7eOy4vqmcj78vSEUACuC51L4WYuz8tiEvnFboy9E3Zm1iRczU74kNZRpGoslzCoxEo5mSmC8Oyo3Wz3nYtSaj6eZVEJP3wsmZ41apXgH+Yl9qaRtCqZ4sOg0KeXan+nnx4lSTgiWiMGsd9BKCzmxsAjAvU5qBT0/Wmb63gX9JJTShiCRVRhPLgQQyXVUgKOWRr/QdZOA4DO/oP7Oj/OjZtyfbL25cMVQlLMg4EoVrbqfEEVoASbGzsjfHK+Yqcf/C+QQ0WAJDriFwaSrmETyWQ1Gp5s0l0ACgT80G/yMUjnx8e+Hy/r33wXO++HiwbNokmlEkKhRURJcm7E28dS328O3S6NVWNHFNeeqFUUjNADu1hmAquTjoMCVUYLBzDs5PzPFuqMdDre2hz0Pn/uIZlHBiGP45LOfvy/ZDW/n0DfcKTM6fvuzAotNmm0b31bOFA8nQpJgalKEIiSp7otuNM+koZLiGj9FEuP5NRuq5matG2heuScV2///PG4R+qHdxJRML+Sy+JBios/ocpKK3kiyT0OoH6TQouiS6USSSYtO7wp0zMAwAuWa8vRp6wbH/y/f4OwWua13gpMokEJpPqxrQONHOb8HLnkZRH3zUbUvdeAa/WaoCNs4nXXqwcaglMCSqFRjRS8+l0C/NJTm9a3uF18XSxauQnFlfyYzPzXiakP/iQXFBeBQAIAn0DfeTH1uyaQK3tN54ffvRW+V4EgZjfFitOZ3oeVr56JevnwY6rH+bv92S17WA14Xnh8ZeFpw0K0JET1sL1dWzaufvRyyd0A4BtiwbnFFb8vniIoe2YBAq5yb0CEQSavvkONnc/n/HqXWlqawvVWdjrokRLCsuFod83HwcAkOLqt2hlGsrlMFi1ds9LTb2irezfAAAMrmHvZjk1X68NS7+CJIipSKnK+jX+yL5QvfziDKLPz4eEEikAsGiUzs3cW3s6dfV3B4Bqg8XRFlmqEmieI4ivkBTQCBxbqieFwEioeAYAyZWvRzpvAgB/TvfzGWv0NFit/WvtSU0d1HbOL+dObZoIAG2/Ro5VBUwapV+7ZndexzeyW4MWHK24ZANDLBqfztbN3Jk2B5MfhJh7KK+7vypKeFOcvMRvgJ4ZLW1p3Gke3Q59fijBpAMdQn3Y9iwSTSSTFgjLXxUlHE157EC3mOyuXiLVxsWKRCEqcnbFvU7uO0lbPghDibgXqzgmkgiOnsb4gnzl9+Hr4phf449INHjBzfYY0d++OrAgR1A4K2K98lUiQpjlMaKPXXsAkOBSTY3UEwqJKJRIj88b1dLFXnk7Lr+8EgCsOTXrCov7d1jcvzo4Nqe0ovemQzRyrWnI6bQVIeaDJJjoReHxsa5b5YVVkmIWyQoA2CTrSonG4FIVdq+oFZbEYVKPr2/YHF/689OMPj/NML2cfF0kUllabsm+Sy+fvvuspdrXteB6giLoL4Hj5kUcXBR5eI5Xby+WXbmEfynzzdGURwMcQoY7G5CeZ7pnjy42za9mv92XdDeLX1wlFaIIakPlONMtZ3h2H+AQSiOoj1onkggtO/pF3K82Kw/PvRqzfKC9u74r/dpJ+5h158QzxWlQl2bGLfx9ZYOVwc9tbxm41Ee3CqU9zepax13KJXuTz2ULChqsa9XIR0k2XJaK80B+eRUALDpas9kX/etCwpf82nyRBACotUN/ZLikS53dQBJKleIiAkKU4iIySjO6n4TGmPXIsLzmiNl+hGKAGyFetgAQFsLRS2bWIEhEgpeT1cZZ/TrN3qWl2oD2avJlNEFcGFbHwxYcT32yNuZsvrCMipJ8OQ4bW47pamPw4rcny3aJ70AAqJQKez74aaJb59levXTeBQD9p3ZRGCyZFFs78o/fbq40s66vGGRaXPaqoduU860OnatXf+rSSAbr8LmX0Z+ycgvK7a05O34cqSjHcSChRq6GFovLWnIbKpuTAjKJAAB1M/3Jp4Sa4IslAEAlq/nTYsruYEoCUlZUtzxBogsjKJefYE11M0GP6480ASsaglo9AkLdQbtMRa4TK+oLUpUBDgGh9Uc41UNIwKWANKDOT2kFX8vVIB/HZm4mcENvHLhkxnyffvN9+umu2jCE9Q8K6OAb87xaoiMrKW9htw0Ld0wK6W7kjqFMil3d/+D4z5f5lQJFYedvWgd39ddylxYayWCduvpW7pGQk19eUFRpraTsdS/v1b28V2rv+j1ouRdTvQMxhmNx5Z/HOjf4P618hCVUY7B4CAKRmxeQCGrWRwRiNSOs6kvSCkzpNx9qPuR+3r5Q86GRJVe6Gr7i3iBgPAAZ6Cd3hVreUr27qJ/RGbSM4OjNt1quzvmmfaP15GvxpjhpQcRhLRWOpjw6mvJI7SVrKudqbX/3FQdmLOy2QRGdU5BZ/P03vwd1aTZwerfWfVrqmcYZAEryyu+feXHr6NPc1FrTII8A50W7JuvZSF0ayWA525snpFT73adnlygbrJ62YQu81Li0aiey9BOTSHdj6psk3Wjk67XDfz+hKLm0bKK7tXlRFc+CyVC2VreiE1acuKl8r1qD1cayWmXwWcExAPBktcUBT+dFd7WZ5sYMbYg/wVBwvBIAADFGsBwXXAVpMsLZoruqKXgTl3H5iUYJlO6h3kHeDf4N+eq0tvCqv4+7Akt7s1+uLP9+6DZluYV3j+PePY6jMSjN23n7h3k5eNrau1mbWXModDKVTsExTMATCaqE+elF2Z/zUz9mvX/2Kf1TDl4nnte/rdePp+driqDWh0YzWGZKBqu41ReHMQQBCWaML+jFrHsDHbrouXVSHyjqjE5hJQ/DcFuuDvFZlSlhawv1Al5erDAvVqMmPdeBLBdQK0AoumuqgBXilVsQ+nggtWiAbqlSwROuO3hbU5A7mUhYMNL4hH2NBobjXR/8KJLp9SvwZtvvDFET925anH3sdj5e+/vcw2/v1noZCHiit/di3ypt9ukPgYgOX9B3wqrBxPo5vjSSwXK0rYkdycgpVRw70+3OZNz+v/bOOjyKq4vDd2ZnXZNs3JVgIZDg7u4uhQLFCsWLFS1StEgLRYq7OwT3AMGSEOLunk3WZWa+Pzbsbjbr2QT4yluePjt37szcJLtn7z33nN95XKg7sWOYW/cJ3jq0ye8XvCqTVfR2rkW9WhVkhAAAODxjeKivuhhJdGY+ACAmq6DxwspCZAv6dfixU0jv4Eqf2rW3sb+duaM1w9IKWejuPLNWR245imSA6AhBNgLOx0onAawUoo/XPiO+jIsvAwAgxmyIYbUK75uOPygq0+tJHNMzxMWq5XVjUvOaT7RYCUMvMAQ97rbms/aT8Z0TZRypTnbG3zyd8VzfWRU0hDy7Xp9Bboai/20cWGvPz3338NPJjVdjXycbvacBECKh6+g2o+b3dfa2woaj2QarrFwkEsuoFJKtOXFfrhqSXdn5aoPVyi7oUtvtZg0gkZ/xb+qltY1mIqbVkqshSpE/adWcXsMed/DZ50U1+GXS1KZfjUdXK+DyaICmA6Aw4+2B5mO8nyGYAyhdsdLxMGcHIKpTWyHqYKsvEu++Trj7Wq8shIs9e1J/HdV5v04gABGskcE+J7DvnEAjGlgYjg14svFFUbxhg6UkpEvDkC4NP75IfHo54tWtSC1NPsNAEFQv1KdNv6adh7fSinGvCSa9I3EchL9Pufkw5t3HTIGoMn6fQSc3D/Ls3zWoZbCX0Ts4a3zX1UQTIqY8eUPcvzP9RvkzzZ8CWIRyhiVTVK0cg6Lg86zq+LP3m68+0bpKIpMDPcvJrx2sGMijAcBxSRhEMcmk4rIXOG8hRGoDsTcCiAAEO7GSsRC1P8SYr2uf0QrkFVdsOv7AQIflP3anft25OF8KGIIRmGBW5e3GbQMatw2YuXVcakxWSlRmRnxORnxuUXaJiC8RCyRigQRAEI1BoTEpdDbNxcfRq4GrVwPXBi38bBytOcNVYvwTVVBcsfav2x8+ZWm1C4TSRy8TH71MbNXUe8mMnva2hhw6TvbqUI78ogocB+ey7pzIuGHKEIe5d5/gNUCBoxez7l/OeTAnYGxruxoVXDALEoIAAKTVdgkNo5xhUYjf3mcGF18BBEeI0gfnb4PI3Y14stA8nL8Rl9yDGLMgxgylqhfEmAeRO2MVG4DoKMRcbOhyixBJ5fN3XqkQ6s1EG9wpqHkDI+pUb1Oz70YnLRvUefC2Y5cXaK9hv6MTn0buPo3M0922OkYMVkRU+opt11WzKp28+pA2bt7hrcuGNtZfzdXelgHDEIbhAAC5HC0uE4z06DnSo0racIVcMPbV0ott/yTpisx6XPjmfVnc1iYL3Gh1qm5MJhIAAL+euPXr5x3Am0t0l7auvktoeEn4NYKLcdFRiDYOoo3FJWE4fwvEWm6ou2A7wIUw9wpAAqqcIAbDducAsH52DobjK/bdSs7WmxLgYMOcM0K77EJuWcXK8/c+ZuYHOHNXDu3m72QnRzEFigEAJPKvRcDLRAgwAYYgkh6JGFMgQghigrPs68TQuD8m5C7dfNWwtVIiEEoXrLsQm5SnrwOBANvbqkMZikr4Zo0SANDNsdWmJnPr2FqBavmAZvHNzbBwwd8AQBBtPIAYMHsLLjqJSwzNgiH2RsjmX21rpQYGQFnH1Gq/hz0XnhtIxIFhaN203nSqdgTZuksPW/q5P101raWf+9yj18xaEH1tMBFKeI8NhitQGOZ8+wWbmv5gxSHpA8Px0Jk7XsZmmHXVogM31p64p++s3k9jWblo8cbLEql6t7VhgPOQnk0DfR1t2LSiEkFEVPqF2x8KiiuUZ4Vi2dItV09s/5GpRzHdkctUdS4u+4pqcBlG6XRfPLDTuPbqOk6fsguq9+wdXE+1S7ju0sOz4VHflg8Llz7HhQchm78BRAUAAFIoxFqB834FHKDfmaXxhYdm4qITuOwdQPMBXgFgBwjxBpT+EHuLJRESurgVHnvUYJjo9MFtmtZz02os5guT8ot3TxoEQWBmjzZX3samFZrhPP6OATIKynZefvY+KQeCQDN/tzmD23to6LLhOI5huAKtMof9edeliPjM6t8Z136f6GbPAQAoPk9+daL3E7XryKPyz9H0MAzNGt9pZL8Q1Vk2k+rnZT+4Z/DmfXfvPotTNhaXCnYcerjiF93R5/Z26hlWia7daBiCIQCZsrNbHSKE6NNRrCE6ne5GUTrdv6UloTwG582G6BMgsrqmNEQbA3AhLjxk1PuOSx/gvLkQuQfMXAwQXwAxAVaIS8Nx4T+4cC9sewzANdUFehWTsf6w3i9eAEDrRl4/9tWxM5hTWlHPxV65CwdBoKGb48CtxwAAI1rpLv7+HRPJK634ccuZHiH1Lq6agBDgw3ciJm09e3TRKFeuIV/728SstRN79W5uYQle3Z+oT4l5KjMEAJg7scvQ3jrqRFIpxJWz+2IYfv9FZfJR2JPYEX1D6vnoWLhxbdSR0yW6qpwyENq19ruqt5uCD8OtNrRlwOclodRMT4f4W3O645JbELlTdR85RJ8C0XX77DRQ4OXLINq4KpcT3CDaCIjaBysZjfM3Q+wtNRnem9jMhX9dlekXjHewYa6Z2ltnbAAJISidp5/BL83/oUQguhdtnnRqIx/nw59VEk3kwdvEJbtN2ln6Ftlx8Zm/K3fp6MrF6dwhHbKLy7dferp1qiGhKgzHdWazmYhug3Ux7IPqdetmPjqtlRIIAstm9vqUlJf3OVjhzPW3q+boCAbh2qi3EUu+nSUhuTIOy7wZlnJXkfLtzLAg5q8A4Hp8msZ+CrQYYGWaUzON+zIgcmdc+rAmUUZv47Lm77wi1V9okkom/jlnoA1Tt9aFiw0rPrcIxwEEAQzH43OLPe05JQJDKdPfMYpQInsYmbx6fBXRhaHtg2buutRshnmRlWah471Yzhc/fKkOyft5nJFS12QS8vMPHVZsqxRaeRieMHdSF3a1dw/X9lsyWJnFvL4bD6sODz58c/Chtvdk242n2248rf5axaITtxaduEVCCO82zq7V0VoDyPJSYwR7ANvisnCIFKJ9CpfgshcQ0XKBlw8J2fN3XjFQFheGoHXT+tTz1BtFzaZR6rs67H/w+seOIfsfRrTyd6/JRsp3lMRlFqIY1qp+lfCR5gHuVDJxwbCOQ9o1BgCgGNZ85k4Lbn79Vez1V7EAgOn9Wk/tW6W+mY6/3LuYTPnnFVCgr5OPCdLOnVoG2NsyikoFAAAFir37mNmljbbwix3HyJLwO98sBIi9HufNA1ghRB0CCN4AogOsGJdF4KJDAEch5iLL7vs+IXve9stiqaE8u9kjO3Ro6mv4PmuGd1t65s7hJ29b+3uuHWGhElOtcnTjtaSozFq6uX8TjwlLKlPcDq27kvop24KbkChEBzfbzoOb12vmBQAoqRAiBNiOVSVDHiHADhxGZmGZ7luYTP9WDdZM0F0qQYfBio7LUb3u2NKkOj8wDHVtG3jmemVK4PtPWdUNFpulnnPpdLp/VXhwOR+3zqvefvzZ+8i03A2je2ntAAoksoXHb/RrVr9fSP3qV/0fQdD5noHIXSHudVx4DKtYDdACgAsAbA8h3hBtEkTpbaJSjRZhL+N+P3TXcKFDNoPSu7XxXziXST8wZYgFY6gzzu66W13bwFq8fxKvMljndxvauDDKtUNP1hyb0bxrw3KhhE7R8Wdl0ykig18wCAyLpDKLB6DjzRebrA6nCvQ1Ne6pSX03lcFSCTNowtFYJJaVi1R+etPxcLENsEb+pGVIFYpt15+efhEFQ1DPJqk9mlQJPrr/MelFQsaLhIzUwtKZPVurpEe/OfhyKZOoLwqBADvF6TkFAMETYq2wlnrGweuv9156YbRbuUDy04azu38d6mxXU1XML0vtWSvr3hzH8Kv/PmretSGbThFKdNgdnkBM1aVbqaJbs4C1J+6vPna3+ikiQni23YgigA6DVcpT+yO9XO0MX6/C30u9b11WrsOjSaepPwYKFFu13ezdEwqZ+ODkl6miGpdTuOx0WHJ+CYWIbBrbp0sj7TXIoOYNERheee7egQcRLxIy1o/q6edk6q/uqyL02uaEobWy32oiChRbf+TejeefTOyfVVD20/qzfy8c6u1iRoYtkQArS+lQTFakq1UgGMKx2rJZkFULX6fG5gAA7NkMBYqVVIjsWGoRBLkCLSgTeBgskbluYq91E3thGI4DHAKQuUW5dRisCg0xU6bJUluaXvZyjTuooNQ4GVVicKpZSxRVCP8OC7/y5hOG4572Nlt/6BvoojukqF9IfQ8uZ+Hxm7HZBcO3nxjVpsn07q3YNMu1yvRhdZ2TDyXZqkA+HIB3xVXyRgkwHGxbRzJ4FULJkt033sSZ580pLONP/ePsrgVD6nuZuiAI9XEL9XEDAHwliYRTVw9NiTHJtXT/3CvV624jWhnoqcK3kTqYdsrqIWmxuWbdE8ewpOiszMTKhZeyWleghwMRIbyOz+zTQh1R9To+UypXrD/1YP0pQ6npAAAY1rvJgxBgov4qRzoMlub+MdHk7x+Sxha+TNeejjX0M+qU5PySk88/XH8Xp4xRGNyi4ZKBnQ3X4wzydL6wYNzq8/fvRSedePbh6tvY4a2CxrQLdjRYDeyLcy3ro6pi3SifZlezqsi2EaA6Mlhx6QWLd1/P+5wOYRY8gXj6pvNbZw9oXt9I2vPXyaApuktvVUfTuCzYaXaGzZBpOuoqG71neYlgVKPKIDtUgQEAaGRi92b+V8NjNA3W+afRHYN8t8+o9JcZ3iXMLuKdeRwVlZKTXybgi6X2bLqXo03vFoHrfuxF0p8iouMEk0FRrekEQgmHZZLulVAj5ZDFND6t0Blcapj6vnVaTeDok3dX3nwCAPg52a0Y2rWZt0kfWhaV8uf4fg9jUjZcflhQLjj06I0Dmz62nd5Atq+BVcG9v/QQwMVH0X+eemQgNNQoIols9rZLC8Z0Htal7sQ8/iOw7XR8484e3H7MhpN/nH44tW8rhAAfuPXqY1resUWjTLnhk+iUxf/e6hrsN3doB28nWwaVXMQTvI7P/Pd2xMHbEfvnDdPaf1Shw2CxNAxWCU9kosHSDK1iM41fcmhzXaRf1oRferdJyC2a0DGkd9N6sJnzwy6NfNvU8zz9IvLxp9RRbb69z098ecGZ1Herm9ZF+RaeQLz+8L3H743LWg7pFPQ8Kq2wTG/mvALFNh1/kJBZuGhcFwPLiu9YBQcO4+iiUX9efDpk9REYhpr6uR5aOFKZD2gYBYqtOX5vVKcmc4eoYzxduewh7Rr3DK03aevZ7RefrZuou7SlDoPlZM/K+CwtmJCS72tCHBYAID4lX/1sJ+sLd9U9DiyGWychzzYPhqrkPSXxcw+n3Y2ryJKgMl+G80iPDu3t1UWQ5n/YH2LrP9azM4WITOwUOrFTKIpj3R8tk+qRrochuLdzyOL6I3Se/SK8Kkr/9c2VRY11Ra5bm5cf038/dKfYWFweAYaXjO86qGPjglL+L9supuUayl6+8uRjak7J5ln97diW1NEwgEJ4oEpBIAhB6FMUwv0Ar5wYQgQXAnWgdR/6NePKZW+bZnbF+JIKEU8g7hSkI3qOTiF1aOzz5GOqvmt1GKxGAS6vI9OVr9/HZPXpbFJJsvcxak9tcP0vrPJlIpMjtifxqzggiTAyw6/vMPdKtXgFjqJYlUVKIj/nl3f/TPbpsaDeEAqBFMlL3ZlwJU9cOsKjg75LCBB8r/MGfWO4lffmSGqNQmNqTkRRBomAuNLYPJn4SNLruPL8fW1GNeDU7gKcL5L+efqxKbuBDCp506z+LRp4AAAcbZn/Lhs1b8eV6GRtz7Em0cm549ec2jSrXyMfZ6uNGAAAkQEgAADkFWuJrBXK1wCQAYTKK9YSWb/JeAuo/yWDZRlcNs2GQX0dnxnsp+1mkcoVr+IyAt31Ri/pMFiN66nv8vh14sKp3Yxu8AnFskevElWHzb60LKGJHGxRJTQUw/EBz1bzFTq2OFXsTb412K21yjy15TbgEOm/vPunl3Moi2iGyL0KEoRYJlBhRcJy4kqlwhxReUxZrjONfbjdWE+G1US4dfLwbdKWkw+NTqwAAN4udlt+GeCpUcSERafs+XXYsr03DVeoLyzjT9lwdtrgNuP7NDd3Ra8PhFa5pSivWIvQK6vXIPQJn1t+kldYv8D1/x8EGF75Q/cl/94qKhcOaN3Q09GGRiaW8kXvkrKP33uHYticwXrLHekwWE0budtyaMpoLLFEfvra24nDjRShOn31jSrmwMeDa4FD/Wsgnp9VIRe14xpKfMsVl/R1aa7ZUo/lhgEsV1xqmcESoVI6Yv3QB7NYGVzpL+DLpZcyokY9PjLKp9mcBp1q41lZhbwtJx6+/JhuSufuLeotn9Sj+s4smYRsmTXgj2P3rzwxVHJKgWK7LzyP+JS5cnJPJw11o+/UDcowK0SXNkPHIN9zK344/ejDH6cfFPIEQonMjkX3crT5oXtIt2YBJLPCGhAC3L9r0NGLlTudJ65EdGzpbyCjMCG14MTVCNWhpmzWt8W9/PfuNHt/pqHdQA+afXxFVlfHYFVLNC+NAMEuVAunJDy50IZkXtBD7emcMInkCX4t+rg1+OnF6QIxf0OI2e4JA4gksiM335wMe2vKViBCgH8Z0X5MD73vJRiGfvuxu5ez7a5zTzGDIZdv4jJHLj86e0SHIZ2CajjTkhS2B0CBo/kQwUlS0AJAMMXhlfHL/qvAMPR291x9Z93tOYtGmBrMob6nztahvZqqdGYlUvmC9RfTs0t09oxLzp+39oIqWdrDxbZne8tT878gZTJBWN67ga5GIvF+9u93IzfibOaTUhlfqJA8Koj6PebUdL++lk2vAADF0nInio3xfnWIPYVxosP4bi7a2aAWg2H4lScfBy8+dPjGa1OslbujzaHlow1YKxVje4bsmDuYSTOiaCqSyDYeuz9t49mUHL1i8KZAcXhGcXgJAKA4vKQ4vsZRHSlo36lVdBssOxv6rAmdVIeFJfxJi44fOhdeqKHFnpVX9tfRx9N+O6WKaycQ4JWz+5gea/pVcSAljAwTB7oZWfx60R33NZ/9kZc+9uXmgc/WnM18+mv9YcPdLa8wnCMq8aDVVI1Ti0kLj9fkcgzHmURyF+eAdjd31HAkOA4evUsateLY+iP3SitMkqAa0L7RyTXjTI9Zb93Y6/CKMR5Oxo3+h8ScsStP7Dz7RCA2XqbABKrP1r5hqfhvBb0RpQO6Bb2JzngYXimMJZUpDp4LP3Q+3JZNZ7OopTwRr9r7b9b4TvX96jS201p8Ks+4lRfR2q4BWVfBHi08aPbrgiaYctvL2eHbEy4b7famNPHvpOsAgDWNxnV2NCNoq6xcZMOunNmlZBT7elYu2xN1JZ+byNvizNUfbt/oPg0AUCgxu1aIJuHRaXsvh8elmzoYWxZtyfiunUNMEgjRxNPJ5siKMb/9c/NlTLrhniiGnQh7d/35p58GtBrauYlVYrWU60QA0SSFrSHEiNBNTcBxPCspX7MlK7nA3e+b9BdbjCEls9Vz+mIY/lhj+w/HQQlPqFPNavaPnUf0bVa9XR8Yhpub91hLlMn4q2JONLcNiOSlXMh6roppMAqKY0XS8ixRUZogP1mQ+5GXPt2vitTqYLc2g93a1MKQK+k/cc/zSwuVryfMO6J6bS5zXl+cEdgukO2IA7Dx473pgW1rMiocB08+JB+6/tp0UwUA6NOmwYIxnVh6KpgYhUkj75g3eO/l8CM3XxvVJigXSLadenzzReyBZaPMUoVFJWHKaRQquQ1wuXKSRXF4ZtmYTefd47ibR5/GvErhV/3oTW3/O9uO0bRD4KCfOitlqv7vMfTXIhDgtfP7n7r25uDZcJn+SqJ2NvTF03u0DTHvuwXDcdhiiUvrIVRIfo08yEJoaxuP/1ieviTqMAOh9nLW4T3JERePDteut84ls9xp9p50hyYcnzGenT3pDhezn9fJwK1JWHYshuMrg3udSX3nx7Tv525S5F115Ar07uuE47ffmuUqcrJjLhnfrW2Qt2UPVQHD0M9D2wb7u6w8cLtcoLfMqor4jML4jIJgfzPSJDHZOwBQhD4Rk70BABBZSy0frmnEvEr+Z/l5A5J75SWCx5ffPr78tlErv182jfIIsGrc2deHka8XGIbGDWrRsaX/8UuvH71KFImrKODYsGl9OzcaN7iFvtJeBsAxHFg6H8cBtil2wAiPNT6MGu1IVshFCyMPiFDp3yE/Uwik5rYBKxqOXvvpVKGk7AfvrlBVe+pK5T7puhnHgTKo50zmk8cF0Xub/1KTAXwl4AAsCeo+LfwMh0Td32Y0iuPmumMqhJJLj6PP3Y8s4pkhzUhCCON6h07s19KK4vdtgrxP/f7DqgNhb+O0a5Vr4efGbexr3sebyPqtBkMzDxzDT++4ffLP25j+mleaxLxK/qXnpqmrh/adYLlH9evHpDeKu7PNspm9fp3WPSm9MCe/XCKVUylET1dbX097VUje3Sexa3eo6x5TKcS7pw1pV+EGPxIyTPy48EhCxQsJKrCneLXljvZnqis44TiOAwzDLU+UBQBkiooWRx6iIaTdIT/bkCqDdDo5BDEQyh+x5/q5trQlaUfuQADSuS++J+nGmcwnqsNQG20vzPwPB96VJun8kWEI7uYYvLyheWEKOvnnuLaovOm40tjjfVskVhQiMDzk4b8KzIyizR8Sc+bvuGKuJ7t9E5/5Yzq5GdROsgwHG+buX4cdv/1276UX+ircUUjIhhl9v2adxR0LT949/dKsS2QS+d9LzpQWVvzwq44qMP8fGDFYx9Pv/uBVqYFNRAgN/Jwb+On+UpLKFCP6h/wyydTACgOOBgxXnMn4DYaQMZ5/MIncBH74lew/ervMbsS2vNptdW7lRvgynX9rMJJKqLIpHmobcK7tMoKx6HMEghG4cor4s3+/n/0rK/fNfv9P9c7RvNT1QRPa2Tesfupydvi5TOs4QTxdaxSePsgzaEb42dvZsZe6/KRsCbi41pQLT999b5a1qu/lOGt4e2WqjZLre+/9NVtd8qN1v5A1lxYAAK7uuSuXyofNq/z4rRm+/cXVKqVAECLhpw2jh8zRTtKGIWhCn+ZtGnutOXgnIaOw+hgWju3i7WK2wqJCUPWPW2u5hIc3XNWyVnQWtW2f4LZ9g918HW0d2agCLS2sSPuU/fTa+4j7MXINQadTf95isKmDp1rzw2KYXYljZJjxNbjpkGDK7IBTOk8ZMVhH0sJUBsswCgVmVkCDgWyJSF5YqSx3ut9BCoEOAGjM7ipFhXfz/vFjtFS2WIWpvr315cQYtlaH0+7FlmdsCf5pmK5oBiKEEGDt3wMOAIWgW9ecQiBZazu8T5dK39OGv8Msu8OaZn2GPTzU0t7LlmxGWFmAu/2jdybV+PNytp0+pE3XUO3S9v2nd+8/vbvy9cUdtwTlla5lVIEqNCpCrjqvTqWSCKUPT7+ICIvs/oPeqk7+7vZHV445cvPNoeuvNZ2wPVsGDuxgkZ8OpmslPwMArJ5LGPks4dxfagVhCIJ6jmk98bdBLJsqb346i+ru59hhYEheevGe3869fajOyjy49nJQG3/fusqQEynKDS+YzEWB6f3+q5Hv4HzW4+HunSqfgWJKzVkDUMhEZQYPkUhA9O8of+Q9qMdqq2mbgjjdHxYcjKt40tTGaoInFmfwoRiqwPWumLY1nWLpiCxBVW6vJjdRZQ46UJiDPYO2f3q0tpkZa4qxvUIuPY427L3ydrGb0Kd579b1DW8NfwpPvHfi2fZHq/qxJsgkcgDApHWV+koKOVqYVZyTmJccmfHpZULU41hXf+fxq4aV5vNIFCJZT+woAYYn92/Zs2W9LScfhUenAQDcHDhLJ1goRKHKJazSaNVcQplU/tfi06pDmADP+3OsYWVRZy/u2pM/H95wVWXmUAX259zju8KWEJC6WPNO9dsv129iAACHUmcBACb5/G3iDYmw3khgtcHaFn9OocsrtCnutOYhAhEWBFZqoexNvqYyWBAEbtz/eOHmewBAA3/nWZM6V8/m2bhoUGZuKQDAy81O3wQLB1ieOKml3VDNRhJM9aA3zhHFWdFgWQwMwXLMvLqqEAASVHelEAkq0+0YMwEvd7tBP/0DQxCG417ulkvI3+upVv6fHND6VMpbsy6nkok/D2275uAdnWcb+ThP6Nu8Y1M/oz/lveNPj6+9yODQpWLZjYqjAIBLu24rzRYAYN+vx9/cifJu5FG/pd/E30c6eztEPomNevzp5PpLGIr98+YPA3d2c+DsnDc4Ojk3PqOwY1NfVRaHRaCY7AOAaDCxHrB420g/9868yk0rUh3O2jjSRB3kicsGonL04t5KbeLUTzlPr73rPKS54ausgi3JpJ1WR4pPzZ+lNlgChRitZrDachsJq6oXECDdf6RhfZsN69sMAFDKE919ErtgzYVjO3/UkoRv3sSzeRNPwwMSKcpxgDGJ2h8/NtGRJ8vTeUntgUCE6uu7BmyPs5lPOzz4Vd9V9mT2xXbLNVsCWe6/RR/V53Tv6WRG/JomJ3YaLSJvNiwiZXpgZSTaUK9gE6/q27bhuQeRmoFXBBjuHOI3omtw03puBi5U8ik88eia8zKJfMeTNe8ffpzbYdXMHROa96ry9Jk7flS9fnQ2/OnFVxN/H9mmvxnbxEF+LkF+Lqb3rw6O5svKfgKwLQAYwMpINgchgpUjpW8cVe+ctO4V1HucqVGBAIAflw2Mfpmkqm94ef/DujFYdYnaYK1qZFL0tlFsObRRA0Oj47IjItO7tgs0fkFVxCgfAECCtX0oVAIzFzW7MlgN+bPp1OqNrewC73RaZ9Z9/g752UojMonBVT/qFvOHycnPEATmj+k09Y+zOA7sOYxBHRsP6RTE5ZjkcFTI0S2T/hk2v2+fyV1gAtxtbHs2l3VgyanQHk0QIkHnpj6GYiZu9lsAnUoSinVMh+lUkrxiHYE6FKFPBAAohP/KK1aTbPZa8dFJUZnpcZU6XxAETVxmnjsMIRKmrh766+DtqrulxeZ4N6ijAiJ1gw4f1uDnKy63M2l7yACuThzL6tFTCUwAgBTVzvsRoxU0wv+DkGkdsGBqXYiFahHs73po+WgSgvi5c83Sn0KIhMNxf2qui5v3bBLaIwiCoAEzKjd8/vrl0PV997UuPLvluuq1X1PvPa+to0X104DW/157qWWz7Nj0MT2aYbK1RE7l2hOhjZLwd1W9tKaO5+hwdVZJ0w713P3Nnr41auUXGOId/y5NeRj5POH/32BVyNWGJk2YlyUq9KI7edDMS1mKS85v1tiS+iU0hANDBL6iCIAqFX3LZHls4herovofZNjJ05G5utfgXDrt8LCh9R2q5GyjGDbq6nmJQrd3jwBB/eoHbuuru9qF0lr94D/neNJOzZYru+/IJfLhC/r98tekX/6aZPHPYhbjeoWM66V7pSkpZAG0BCBMAACOlgCYDQzmEtJ01UZWorNs8qcItSRh864W5hu06d1EZbCiw5PqMr6hDqhisHLExa7USk85juN/JV2+mvOcAMEojg117zDDd6BR9zCG44VFFaeuvBEKpS2CvSwYEAQgZ0pAmuBDfZZ6u1qKCbNEnxo5/1/96r9yLozVEcvKE0smX7wskErdOdqzXQIMx8ybre9uF2M+/RVuJAwSlWu7UDEUQzWWfrzCilMbr3x48LEgsxhVYDYO7Aat/ccuG+zZwLibzCogtLGyiuUk1moAcFn5KoQ+HhjMJQwJdG/Z0PNtXBaqEYgLQ1Cgl2PnUB053tkp6pCxRq38LBtkk7bqqBFN/31tI8PEyfzXUkzEIjpwSE62JBfosxgMA7FaUeEqBmv8qw0POldW6Lye+/JtacKe0HkBDLdEftb62JOuVPuBrnrTYncdfHj+xnsYghzsWa1DvHetHUkwFuWgjyY2PR4VHO6CTqYQKpXtIsvukGFaIMsMB+R3rItQJjsX/XH3q9fN3dwODx/CIJm30YbAMBkxEkNTXsLvQRqj1agKawAALOnzR9cxbddeXcR1tYVhqLSA9+Hhp8W9Nhz+9CfVhMpyNQdhTAUiqqx8OQCAQB2A0IzkJ5AQwt8Lhxruo4myRqkSOyeORWME9i5qpZ3ykhrpbZjFlew/UgTq/WUqgelFb+rPbBnIavdLQI30jjTR+x66mP1kfr3h9ZjuAIB6LI/59Yb/mXDOgMGaPbnL7MnWmQEFcbrH8B5eyPq9t/MvLKJ9XMWzp4XHBrj+SoKpxi9WoX8yWLOgJWuC4wDDsa85QQQHIDov/1ps3IWYT0KZbGijhqu7daXqr3OpjzKxmEsz4oNn2TJOZ+zWbLm445ZmDDdCJBAIav8YDMEEAgz0lhCuDSCENl5nNJZVEFZoFF3nWCgJydSILxWUm6RBZhRFtclvdbJEnwAAzlR/KSosk+WLUX5cxdO4iqf3Cw40s+nb3HaQVaK+9b7z8sWl9VjqSNlAlkeBpKx6t/Ubr6Oo2gAQCNBvS/rv2fuwuEQAALC3Z86YarYKKgTgER6/Pyk8cipjqQQVOFC8h7mv9GaYt/dvQBZa5x5QHVBaIcou5KXnlX7+V5ZTxEMIhHmjOw7rXNPahWPXnjy5YqxVxgkAyOPz3+fkvsnOeZCcIpLLe/j77ezfN66w8HpcQovde9p5eXX28W7j6enGZpl4w5wKvjvHSGcyjaQ1w4IJ8KLDM1SHqy/MP7H+0rWeGwqzigEE2TiwG7YJ2BT2G5XxhUXxrQWNQan4vFUlEki0QttNRCxUZ8kQqn0KCrNLSwvKDVyu8n+pwDBcczdAn19IhokBAOO8tiAQCcXleeLEJP7r+IrnPHn+86KT78tudnGc3Jito+60Weg1WLZkVraoyJ9Z6R3IEhVySAwAQDQvRRlfKsMUEAT9MLaNppdBuQw8f+nNgX8mQhD4afphCwwWAIAIk7s5TevmNM2Ca5UY8HdGpxiqEFVDhGJZEU9QxBMU84RFZYKCUn5ecUVOcXluUblEpsMhLcMU+y6Fm2uwMBxXzTTKBRI2gxKfWSVj7tLTj0M6NLbsRyiXSPoePtbQ0bGpi/OGnt1bebgTCQQAQCcf7xmtWqaXld1LSrkWF7/t2Yv7P01kko3IEytJLSlt52VkE+ZI3HbDHbiutnP3/GTiT/EtwrZjqAwWr6jCMoPFK1IvAznVSm/83HWD5jyuOvP6bTV8fxLFkMglApEAAASI6EZr6EZr2MlxYmJFeHjx2XxJ8o2cbXHlTwa4LlK5eixAr8Hq5dRiV+LFtUGTOUQGTy7YlXixl3MLAMD8yD345zVViG2Ah54Aaz/fL7yjx9b/rZuaU7Lm37CR3Zv5uNiSiAiGKwEYjuMYrkAxBYrJFagCxRQoKlegEplCKldIZQqpTCGRKURSmUgiE4nlQolMKJHxhZJygYQnEJcLJOVCsVSXVTIMTyAuLOM72JhR1qXtz3+9/KfSw911/j9v98/T6rD++H2LDRabQnk/e6a+s142NlNahE5pEapqOfEhcvX9h0Zv+yQtbf2jJwCAXQP69amnnU74HQCArRMnK7ky+DbubZpl4lZxb9VTJBcfbfVtw9bKFOzMKZMMAageq20Aq8370ptPCo+kCN4eSZsz1H2lPdlIALk+qhgsVZ4NAGCUZ5eY8tTR4b+7ULm54uIgju8Yz24AgJsdNmI4BgBAIAIRrnJ5fELe/oNP/tw8CnwFeDkbki648SL2xovYOhuMUVJzSs0yWJqlHKzrkjPR+qjYNaDfuKbB45oGa7XHFRb1P3o8es4vNJJx1envKGnU0jfqeaUo+fun8T3HWCJX++6x+o0dWAsypC26mR1vAQEoxLafH7P5hay1hZLUE+m/jvHcaFmmThWLM91vgOo1GSZubjI9vCQmW1TsTnNozW2gFLTTp3qeX1C+9o9rM+ok6AOCIAjAsJ4kISX1PL6loK30vJJWjSz8zrEu363PFyS4fb2T2ypF5cJvRZUWlNs6mhcsXZhdGn47SnUY2kVb0cjGnllWZOHWIZlK6jCg2fjFFhZ/YxMdx3ttvZqzOYn/6nTGsrFeG+3JXubeRMeSUOVUgyCoLdfIsuLxk3g2mxqfkHfnXsycmd1bNLdCfqNRIAAvaWCkyp4zl+Xvbp+UVXdxKDUhI0/HhsZ/h2O33hy99aZCaE1NpS8OBAEigjCoJHsbhrezbXCAW+cQP1uWob2/wGbeNg6sssIKAIBCrji7686M9SPMeujRjddVO3ou3vYNqn0eD4avrjA/BQWCIBKFyLZjWJyor4QIU4a4LbuQtTZF8OZs5srJPn9TCabu2yjRsaF+uZ0ZiXI5ebx7D2JPn3sd4O/U8HMSAJmMrN1wbe2Ga1+25Ff/djoE875OkDqRAfk6EUvlf1949n9mrQAAOA5kckVphSghozDsVfzGY/f7Lziw69xTVL+aK0IkaAamXz/89P2TONOfePf0y4cXNUoaz+5Z3b5QGRRHdzutf4uvPUgS8Ku3q/45uNlyuMyZ/159HpcOAFh59u6ViE/AImAIGeL+mxutAV9efDV7k7lCWjpmWExEO9wpsiw52EZ33O3YUa0AAHN/6f7P/kc/zz7+985xTAbl2KEpFRUSAMDUyZ3MGo11GdqlycVHURn5X/vkpbGf85ieX2O57NdZWZc/xb7Lzs3j8wEArf/Z58JitvbwGN64kVZeDgDgXU7uyFNntBqDdv6lfEGAoKGNG23o2b36U6RyxdcTGVeryBTo8dtvywWSFZP0imL2Hd/+7K47Stc4juPrJh9YfnBqs47GRQTCTr7YvfSs6tDJk9t1WMvq3c6/jH6VmLVtQhW9M7kC1fSKrrvw8PzLaJXIGgGGX6yfQSeTZJ+7yar2NxcEIg1yW3owZWaa8MPr4gutuMPNuNaUTgsi96gi4HVCIiFzZnXf+fe939dd3fzHSAd7loO9eTO92oCEEDbO7D972yWzKiPUKjAMOdoyPZ1svJztfF3tvF3sfFztjBYu1klqbgkRIcirvm8qRBIMw1FrKBlsf/7i6LsPP4Y029q3l4+tLY1I5EtlicXFN+MTRpw6vaJL5xFBVdwFIa4uyb/O13e3s9Ef976O0Hf2P8W1ZzH92jbQp7pDY1Jmbx79x/RDykOxULrqhz2DpnQeNacXnaU7cLowp/TIhmuPLqnFowkIvHDXeJ3qfSKp3GhqelxO4ZbxfXs0MbtApOkwEbt+LvPPZ61+XnyqAbsTi2hqLWGrVSsBAMyY2jnmU07NFrlWxs+Ne3jF6F3nnt5/k4hhdfc9zmZQ7DkMew6Dy6E72DCcuCxXLtvFnu1kx7RKXPuwjkEbTz0EOAAQ0AxfmLX9khzFIAB6tTRb2EeTYqFoz8vX+4cO6uyjdoJwqJQW7m4t3N28bG3WPXo8PKix6X9qCoLU5e//K+fCoygDMmEdBoZ8eBYfdjJceaiQoxf23L994kWrHo1bdGvk7GXPsWdiKMYr4qfH576+F/P24Sd51WCa8Yv6N2yhu+xeqUBkwzCaMYIjtZ994cdsUY/VNqHixYOCA4Pdlpl4ldpgHUkL0xTShCGoAcurNbeKG+h23usPZUnoZ4FgBCIsbaCOriaRkGZNq2x1KRTYF/fOONoy10/vO3NouxfRaR8Sc9LzSsv4Ih5frK+eiiZKvymZSCAiBDIRIREJFDKRRiExKCQ6lUSjkOhUEpNGZtGpbAaFw6j8P4dJtUpJYQMsHac7YvjYb9q5eDVB31cxBAD0FdSU/HZ5FplquJDwzD9GioXSJ1feqVqEFeIHFyIeXDA+Sx0+q8fwWTqW3kqyisub+9VKrngAs425LvkuDpOT+RH5kmQMV8CQSZMndafYinQFhgIAongpTTi+Ukx+Ofv5jQ5VlGf/SrwUYhtA0ohs0ErNUaJM0AEA9Oy39UHYIrN+hlrCxZ49vGvw8K7BqhYMxzEMxzAMxXAMw5WuUAIMwzAEQxAMQ8rXX2zEZjKsU02TezTh0mnTWrWYe/3W5OYhXXx9vDg2ZCIiksmSiktuxiecif64rFNHa/1qyEQEhqH/1PxLLJVnFfI8nWz0dUCIyOI9E20d2Jf3mxEThxAJU1YPHTCpo74OOA7epmZ7Oeh9roo5h6+pXhMJhIiNs4xWbBjqvtxwh+pwSE6jPdc7UfxNtFZA02BtbjJd+aLro/l/Np2JA7zbowVavaWYfG3jyZotmcyS6h4TlU6D4XchhkufZffBcCkFcaYhHgG286mIqWJjJw88YXFo/YdbogCbmVq0c8P1bf9OggkQsFRSQot3ydn3PiQtGd552IZjF5ZVSY5FcfGzrN5StBAAgMDM1i7naETrh1wtHWvlCLiF7du18/S8EPNpzrWb+QK+TIHSSSQXFqu5u+uZ0SMbO5mnj4bAsL53PJVMXPxD172XXpTxaxqE/Q2RnldqwGABACAImrpmaLt+TfeuOK9SPTZAaOcG09YOc/M19HdJyS8RiGVXI2KndtMuXqu0UK0CPA5MHwoA2DlxQOdGvsqcFs2vbZUha+ZtHV1Ad5p5Yah6DZuJc359qTmmIJSnSxR5AAAZWloh/eTOHGG6wZLLUaMZ5BdPvty/XV0ZgcGk7D0zw96RLVegCkWN3NKrT9698qpyW9fDnnNt5URlQg8AQFptVFJFkdJaAQAUGF+kyKq5wTpx953hJS1CgMf1qOnOYysP91Ye1ikV1TewXt/AevrODukUNKRTkFUe9DWAYThPIL4XkbD15CN9ffimhXE0aO6z8/ai13c/PrgQEfksobr6AofLDOncoOfo1o1bG/eR33gfN6JNUEZR2b8PImb1rhJGv3PigC6Nq7i9IF11g5Xdlpy8bcrga4OaOt0fPIotLlbvwTk4MDt3rG+gvyZCebrmIZNcIz9xdYaObT10bGvV4a9Tj5QU8e3NDB3WyeqxPVaP7QEAkKPogN+P1PyG5kIiIjBsyF4jhC8ZAfcfB4YhWxZtZLemFx5GpeeV6uwjlJgqGQJBUKueQa16BuE4XphdWpBVKuKLAQA0JtXJ087B5Oq5pQLRpVcxJ+aMQgjwyD9PtvB3b+FXR4ULrYipBmt30hWd7es33pg/p6fSs46i2NoN1003WGUStdwXg+hLhM1JqoSAogaRIIbJKD+aULaNAFEIENWR3r2+nd7FeWpeaRNvSzJUa8iIGsvRfG0cjXu/JuI+huMIDG9v36+/t6nvorrkePyHVa/vKQe5o32/fsYG6WDD0Gew5ObP8SEIUoZxmnuhkrUXHvQPbeDB5QAA1o3uufDozd1TBjX20CEbD0GQRG52Dn/dYKrBShHk6GzHcbxfH/WHZ+t2U2sO4wArFKnLCtCI5gnAe/s5blpx6d9d97TaEYRwI3w5BEMAgNtX3v/1xw2Vi43BpHAdKqPD4mOye4autrNnnrqt7acDAFTIYnFcocAFCiAoFb82MIxrEbFh7xLC3iUAAIa1/QKLmoTMwpTcEgCAnys3wN3UYJavkJTyEmWkogLDUspLvvRwdJPEK1YNMtmEQRoMYanTTYY9d15mFPE2jqvU1O/YwGfBgPZbrj459svI6p1b+XssOxW2+MQtzcZ1o3vWxUCNYarB+rPpTABA10d6IwM1WbH6kgLFMAxn6td4KRG/kCjUZexKJK9RXEKATFVi69C9YYfulSEXw7tuPnZ9LpWmLYCVlV48ZW6PwaN11KEMbOS284heZSVcoz4jBOlN903OK4lIyHy7Yw5CgF8nZN77YFK5dmtRLpDM231VKlM0C3DDcfz43XdMKnnrzP4s2v+Jmt13rEVhueDW+4R/ZwzVVKke2Lxh/5AGOvvP6t1mVu82yh0zTY/79bdm5AlZzIGUGTjApvru03nWCoGjqWlFqiWhsmXOrO4KFIMAxGTp/fCk8KoMCMVE2fzznqwfaj4eFRAEcIuSPjCgGY+m22DllVbM2Xd1w4ReRrd7a4kVh8JaN/Cc0l9tjv+5Er7837BdswdZ6xFdL/9r8WSHihCd6cw2zp5zm7TlUq2gjfsdi3FgM24s/bF6u+GoHdNjenYljpFhlqSCkmDK7IBTWo3F0gwDl9TUYI0c3uLoiReqwyGDQgAAXK4RaadC0QOe5L1WYypvnytjCAIbeXPv337n4knt+iuDOmzQPGzRLmDtjjEe3vY71l3f92eVEuoMFnXzXiMlYzFc7RDVOcN6Hpu+5tS9hUM6NPGuUSXhmvAmPmvLjH6aLRP7tOg89x8rPiK9Qrf/xRTECnlqeWlqeWlcaeHFPuOsOCotVr++fyTunfF+eoAhiEEkBdo4TG4Y2tPju6ygIUgIQafyuEhRbm4OsxIFJjX3ErXB2pl4QY5VLoW2xp+VYXKkWpX26kyfYrYCMoqJ4ko2VG+XoaWJZdsa2K00fPnUeT2nzjNpOd1zQNOeA5riOA6U/ymFtCAoJTHf8IUKTL3vWX2VimH4tstP1v3Qs2U9SwovWgsui5aWVxqoIfuVllfCNahe8kWILS003qkGxJXV6P4YjlfIpBEFWREFWQe6Du3ubmFxrW8aoh5LpMWeKYOUL7Qs1xS/fQpMe9PzUOosAMAkn78NPRc2O4tWbbD4cjGKowCADvZBQoUYgqCpPtpKXVZJyIgtWacMv1LiSO9RILyrfJ1VccaO2saRZrxw8bY1V9p3bdCinfGvxFE9t569+6vmyIkIATGoe4NiasEgIqydxQ3D0KVlE754yuSCkZ3m/311Up8WDb2dAAAxafmHb0UsGmPN8FFftl0ir7iGN3Ghf/k0eBM5kxj53zRYR2aap7r1+8gqahN2JL25PpbJihpAbbCWNzTuP7rQbk0Nn5cruJIruKI6tKE0C3bYHlk4T2WzPhYtoTofY5F0uwNVKBSYicGfErFcq8XDx37bgYkGLpFjaklGncEWX9xaAQA6NfV1c2Bfe/HpaVQqAMDb2e6vuUN8XaxWsRIAcHfQZOOdqsGTSgbdPJZeUanq08mtdjUdG9k5vc7Pssqt4su+DbnH/zLm+bA4RMvLXQAAeJIPn4pXqw4hiFDfbgUAUAO7lWWSdzK0BACAYqJ3+VNDnQ4ySXoDowEAEARMFFHBcRzHcMicrEAZqp5WEAkmRYchhMrUE3Idahb6uXLnj9CbOPalECtkucIK1eHoAOuHjG0Y99fUTWO5rrYAgBXNu/CXv9x85zcAQFZC7uTGCzV7QhA0f//UnhN0/5ZwAF7nZ44KO608LBZbp4rfd2oPkwzWvHpmKGzpgy9LfF8wQ9Of7W8zV2mVSATbpo5/v8n7EcOlAAAZWhqRNz7EcS+H0lTf3bz8HNcvPY/rylX869jUgAZqX7inj32vFjomhgtWDuwxQMf9FZgQxdVbHiSCSXOWED+3ED83AIBWImHd0GrGrlefi+h8cVa9vi9DK52hbZ09/djWnPQp4RVVEMnqzRC5tHIS7V7P5a6syq7Tu3vRyZF6d50gAJrZq7PBVD7c79QSpbIcW1KNkhBNMlj9XFob72SQClnsu/xpckz9xWtP6+zNnqQ65JCbBNlviiycpwyoU2D8t/mTGzts1ufPGjG+7YjxestQa/LXsalmDVWV96eEinyxfUADYBiuuS8jV6BawrsQgL6I1MSDrJS7mep4tDnBJv2NzKWimM+yM2myLxZKDffUlND5Chb6/89kCKNPZSzxogeP9PjddHkGLawp4KePEnF4ZOEchYYnm0Hyb2z/h9Y7xJHeowF3ZWzx70qbheKSyILZXuyJATbzIYMFcqyLSF7FIUJBvkDmjVGGrDiiWQDRgcPot/igZgcSEbm6wZCfrjYolYh+e6kOIunrFdjC0frZaqX5vKzEPIlASmUaD5EtyirxbGBI/kmsULs4qYje4rvfMRcqoUpsE4rL7+XvBQDgALPYWoE6MFh5ghsfi5fhuPrTRUXcQp3+rb77BgBwZ44kQNSYot9wUDk5Ty8/zJNGNeaurw1JFp2IFVUM1tc5w7qyvq6NkVEwHJ/99Hq+qHK/gkEk/9bckqLfRnl4+oWNA/vOsSeDZlaJbvnjh78fnQ3XeQkBIdwSHNPpxxTI1aFANuTvSQJWY269sxpH+J283UXSdAqB3sdlbk1uW4sGC8UlCaWbsyqqFCagIi7NnQ+SCXqz3lwYAxCYHl34q8qRxJO8f5EzyJcz3Zs9GaqBbTYRgTxF9ZpEsCHCnNp+4v8HWz88fZ6brjpc37qHKqAhtby03/UjIoX2dq0FCMqEl/8K23Jv+aqh2zoNb81xUH/tLT0+a+nxWebesECkoTVCq9GeUk04vOFqykfr7HXWNr6N3ScuG2jWJffy90Xx7hJh8nD31RyijnRr06mtzz9fFh9VuFAoT9VsZJICQ5z2GbBWShxoXVu6nP5Q8ItYka1swXBpUtnOPMENf9u5DjTd6sDWolwao3rNIH29oc/9lxysLonlzGUdWqwjnbW2ORL3bk/0K9XhML/GA33UgSm5wgqrWCtUgf4+akfPHzu6+DqOWjRg/bhdG64v1vS+ZyflXdh+c+4edZbou3vRKVEZIxbqrf1ZKFZ7Khy/nME6//c9y9LI6p73T+JNN1gKXHY3758o3h0EIg1zX+lGq2nlvUqD1fP+zmxhGQAAgQkf+i1fHXV9bv2up9IiOjvVa8gxb02E4dL08iMpvD0YXuU9yqW2a+Kw3WjmjRImqV5r1/PRhb8Wi5+rGgXylA8Fv7DJjf1tZttRa8Wbi+FygSxBYxhWluiyItc3VgZJhUzZ/u7APAAAimEtpu2s+5FcSvm05rVaeCPUwW1D61rJ7M9NKWCwaT+sGAoA6DqmXdST2E/hicGd1Z8BHMOxqkYcw3DDsreaMyxH6hczWN+KtQLmDLVAkno1Z1OJNIuB2A51X+FCNRSoZCKVBotLZtzpNkfVqsAwDOAYjitws1R78DzBzcSy7ZqB7AAACCL4cX7x4Uwxax+GCLNDnPZl8c8nlm7RdNiXSz++zZ9CJ/p4sEa7MAYgsJG8RbPgyxI07ezX6cDSh1Xq8ZjLqYTIFa/uqt7Cbgz2/i5DSFXlA13oLBpCrPkky72ey8pz81SH8/fp2P8NO/w47PBjzZZJ60YZuGeWgKd67ckyLnZeS0AwpDNG5ytEpyvwTekVBSajEBi2JFcHineZLO996Y2Y8kc4wNxoDQe7LWEg1oluqTRYNc65wYvFL5LLdmmup5RQEbcg+40cSjOLbgu5M0fYU9vHlqwpEj3VPCGUp8aVrE8s3caldXCgdeZSO5AIVni3VVQdf5n4jXUFJP7P2Pbh2V9RVfzcRWLh64LM3p5Vvkt92Lax44wIE618de9YvHY+vAX0nNBxwYFpqsOI25GpHw0JoieWqYOEfWshZMxEpqwakvpJt+Tc14ZPQx2BVE8Lj8swbUl+KoHVweGHpjZ9zDEvRqx2TX1Ycqwil385k39GJNcOz4MgghfrR1+bmaarXOmEgjg3c9xbIn6VWLatQlqlQDaKSwqEdwuEdyEAe7B/CLRdXJMHAQB40g+ah8Xi52apdNUlOUXlqtq8WYU8AICBGuhWRyiXLX1551pqrFa7FFXMfHx1efMukxqE1tlgVG4sBof+6Gz4naNPVKcgCFr47zQ91wEAQFK5psEyVW7Y6mhWqP8WacDuJFSUSVBBsTRTjFaGW8oxSYk0W6AoZeqZXt3M3S7FqmQXKK0eEdb7idNrsDqGbQUAdHQy5HVOKz+YUrZbMy5chS2lZaDdUqb1nNZ21FatqefyhXfSyg9qmS0AAA4wsTy7ho/AcbRI9ESzBcUlhaKHzvQ+NbxzbbD+xAOlN6FFfY8NJx4oG/u1MZKDaRXiy4p+fnwltVwtPsMikWkISRnTgOH47xEP3hZmb2jdi1MngQLKvBwAgI0j+0bFUdMvFMpluYLKTxedSHL+dvK0vzZ6O/+iel0uL0zmR8RXPMsUfXxbevVD2a223FFt7EdCQNtlEc3TVgxW4krVqz2t12A96bXwVKqRqo3l0ujq1opFbhRgM6d2nOKQE72XE70XT/I+o+JEgei+ZniXN7umoUmlkteasfhKMitOfZ0Ga8+8IXX/UAzHj8W/3/j2sQRV/+Zd6Kwj3YcziKQf7p5TCf7dSk94W5CzqW2vzm66SxB/DcSWFqpWIIE29v/BSPdC4U2RPNWL84vxribDJjqE2PYLse2XJ056VnQiRfDmadHxZEHEANdFNqQqYdg9nH6WVZ1hQRDMQGz9mTpUgpVUGizMPOd6JT7sKQXC+6plJ4fcxIsz2RRxmBrCoTTjUJopMH6h6GGB8G6xONyO2tJSN5maAqEOe8+TvOfL4q24XfguX2+qEJ3o08b1sj6N0y9OEq948Yuw90VVXC0NbB0OdxuuDAi40GfsxPsXIotylacKxYKJ9y90dvNdFtrJn8P9AiM2RnSxencoyK4yPggHIJPP+1iSH12c964wJ6608EDXoW2d6yhuuQ6IL15cILis2ZLB+0v5gkxwbOX+zFoPcqb6j/BYE1fx9E7e7lxxwrH0BaM81mkKzoTY9jNwuU4qDZYEUzS8ulr5OtjW3ZNukveRRW7kzOibL7jtQO/qxf6RQw429/E1AYGZLoyBLoyBOI7WXPAFB1ih6IHOU6m8fU0cttfw/qYglKcqMIFVdg+si1Au2/Px1f6YCK304E6uPrs7DaQTKzNabMjUUz1HLQkP0/RtPcpOeZqTOtK/yfTGLT2YnLoctlHeaRjfPBF/SXhYQllRIq9YKK8iRxddnPf/ZLACuZsCuZuUr2tjhqVFfVYHJ4rf+aw1JdKsk+mLR3uud6Za7imqNFiXOk3H8Mp8WgIEL3t/xcTrG3HX1bf7zawKXVbHKpmGZZI3Ug1VGQ45mCeNVL7OF971seok6xsCxfEziZF/fnheIqkydUdgeEHT9tMbt9L6oqAhxF0d+rdycl/z+oH087IRxfFTiZFnk6J6egZMadiiqb11gkUwHFf+U+AYimHK/8sxTIoqpKhCiqISVCFF5UK5XKiQ8WXScpmkTCIukYjKpOJSiahMKikUqYXPwjIS9T0oS1BulQF/PTxNr6/KfgMaM6yGDnu4tbBCsiG5jPPafDxtYaks50LW2kk+f9ERjmW3UvuwYAiyIF8dhkgw9P+QMppZcVr1GoEZzZz+eZE98LNyA55UtqOZ497aHgOd6IXA6thFOpUkFOsot8moVh+oNkBx/Fpq7N/RL6vXofBgcv7qOKAJV29a+JiA4Gb2rnOeXk/QkMRDcfxWesKt9ISNbXqNqoFIVkp5yZCbJ8plllQ9sIyvbWJYczp4Vda/qYMZlhIagT3Kc/3h1F8EipLL2RvGem2yLJRKt9MdgWECBBMgGIG+TEmYOkaiKNBcDzrT+xBhtq/NjNjiSiGtItHTYvEzLrV9zZ8V4rSfS21nSs+fBrT+99pLLZvF5dDH9KxpDXrDyFD0QvLHvTGvM/k8rVMQAMP8G69q0ZVBNKLGHWhjf7P/jwc+ReyMfKHpoQcAONFrFOubLSivbWuFwHB9G4cQB9cQB9dQB9f/p91DqSL/VXYHrUbVDAsAUN9+uwO9b208mk106Ok880r2RgQiorgcsWiio9tg/R48AAAwM7BTTcb3DZFW/q9mLUJ31mgAgBtzWFbFab6scqUQV7Kures1GDJbNt9ixvUKGderdm2TTmY8vvwgK6V6e6CN/dpWPZo7GlJr0QSB4RmNW/Xzrr8u4uGdzMpfY31bh06utSuabAEUAuLDtm1k59TYzimI6xRoY08m1IXyUt1DRpw6eiUmFP/mzBzOIgcXCm+K5RmenJ8BAG9yejZ3vWP0DjWhPquDg6+3Hdly0aH/z7+KWUjRwmz+BdUhl9peqYMKAUKg3bI3eT8q20XyrFTePj+br0XYs/agErS3KRlE8rzgthPqhyDmZ/+4M9j7ugyOLs7f+uHp05y0uTWW9PNgcuhEkpZf3ESIMMGeSnegMRyodAcaw5PJ8WPb+bLt3Bhs+GsQ6v/2uZs1QoqW9/fSa/gMWKtr6d3YJP+OLpWl6m5k9GYQ3Tu57Nfs891ggaSyXUppZiXeHHWuvy2lhStjcM7nPeDU8gP2tE5s8hcoSV+XTG7Y/EZ6vPI1g0geX7/plIYtbMjUmtwziOt0rPsIKaqo+czFm2X7aew8GYpiAIcAgCEIAhAMQfosTo8rB1W1f473GNHK6UsWZ/t6+JCnrpSTztsBACATHKt3u5XRX6jI1XUDiEKwa2a/xJVeRfUMxaU6I8kBAB+KNyWXn9Ns6eJ62I6i/jShuETzk4jiUhTXLlz4XzdYFbLYXP4V1aEdtbUtpblmh0C7pSWSV8p0bhxHo4sWtXG9TIBq9Omte+gUvStZBlX7VFN7l1AHt0Re0cT6oZMahrJJVotWt+I6Syu/+uuERtHrpqHrP1U3NHU+x6oah/QmxyyNDVyCFheIXmkZLINU/0Yxe1Zr3hsIw+XhOYOE8jRzH1PHkAn2rVzOUhAjUmE4QGOL1+BAFTQLBdgu1OqDwIzG3A1v8icp42NF8szY4t8b2/9h/UHXJs0C3Vo18nwbl6UpoQVDUH1vx84h/tX7H+w2lEJA/l/9OHXGiG7BH1PyCsv4mo0wBAV6OXYO1fFr1yThQ/rNo88yEvJkUjmDTbN1YNk5cTwCnDwCnL0buFLpVval4rhcZ/XmPp7XdfaPLN6WVH6KS9VbJqY6TbmLmnIXWTi+z5j3jlRggq/fWgEApGiRWJFr1GCllx8tl35UHbowBrJIOpKYbKktPVljMypOKA9zBVdZ5IaerFosv26UXHH+wqhVLCJzb8hWU/qTEMJfC4aa0lOKyX56M5cEEw82/wLSWl8Kvkza/uJentTKm4/N6rnd/HOKBRfGvEpePGwnpqeQHUyAfRu5BbX2b96tUaOWfgSkplv573IHCWTxDnRT487lmCBTcItM4Jg4vUrknYgq0RF6jcA0B2rz1o6bTM/u+O9+hfJlCcll6t1cEsGmnu2v+jr7284vFr9QGeuE0k1MUoAtpUUNx5Ahyl4avVbnN5sSIoy4UJym+U7wpleJtJZjchzgElR7hW8BZ7OuXMm5Nd5zRG/nbgAAFFMocAVWh8IPSgI4XBiCMBxHYLjuZV5KJCKrW6uacOGf+/qsFQAAQ7GkqMykqMyLex8wOfS2fZv0HN0mMMTb9PvX467XPAxxuWLW8CKLt0pRXjD3V0LNNs0VmChf9ALFpbVlsBCYQSN6VleS+dogEeyoREPlz1BMFFU4X9PDV892sYGcGAJECXbY+SpvFIqJAAA4jkYWzG7hfJxBMjKxN4zRFbwcU2SIsu8XPJ3iY6os1628+8czzuk8BQGIjtCGuPXr7aSWmZaiUgCABKup7cNwPLI4N6akIJlXksQrLhALhHKZUC4XK2QUhEhHSHQiyZnODOBwAzjcxlynRnZOmj/+uMCm4wLNWF98heyLeKMwx9AjMDytRXOdp5KiDGl4acLnCcNOhoedDPcKdBk8tUuXYc0RYu3OQpLLz6Tzr9tTQ/zYI3CAXUptq1lsFABAhLWFWwM44wI42iuSIvH7x7lT/NgjNfsXS6LOp2iE8hC1txTN+9lgiNje7bZZlxigWPxcMxO4g/s9KlKjIoumE1O8XHNt60jv7sIYYPgSBskvyH7Th4LZSmeWHKt4m/9TS5cTVMTyoBIPmtupVvv0ncVw7PfYrQn8ZBrBOj5+HOAChfBVyVtNg1VznuWmXUmNfZSdUirRlnBTIpTLhHIZEIO0itLwvMpvOwcqo4u77yCfBl/Dtp0dhcYhU2o4yXqRkWEtg1VRqhZurh/q7dPQrSS/vCCrJDetSKor+QEAkB6fu33+ieNbbgyf1aPPD+2Q2qlAHl92+GPpbhgitnBYAwFYw/9rHnx5xsuCxQyie0ObGWZd+F9cEqbwducLw1SHFMS5IXetKRc60Lr62fycXLZbeShFi97kTW7ufKSWlJRhCC6SlgAAfBlmzPb7OHfr46wjHQwH+PrYPz9VJDiSjRQBMZ0HWSk7o55HF+dbcG2hWHAmMepMYlQTrvOsoNbdPWo0V60hTBI5cvQco90My6IeGz7M8OVX4+IG1K9vysaYQq4OY155eBqHW5kbgKFYVnJBUlTGh6cJkc8TSgu0kxyL83j//Hbu2sHHk5YPatPb8vyn6lTIUj8UbykURwAAMFz+Mn9xkN0ce2rIUJ+Xmt1uZw6UojzDtyqWfAjPX4Th0rZO+xC4ypcxl9Kks+sh5eur6TpEDf9zBitXcD25bI/qEIaITRy26SySqBNfzs98WaJKiEasyH6dOzrEab8y1tS6JAvSSmVlAICdSft2JhntbgQMxzNE2QCAYJvGNR+bBFWsenXvbFJ0zW8VVZw35eGl/t7117fuySLVXSJB3bP12fNgZ2dPDsesq1TWCgAAE2DPes6e9Zy7jWiF43j8u/Rn198/vvymrKjKRmROauHaSftbdGs0a9Moe5eain9I0JL4siPJFWdxHKUTXVs6rM0U3EkuP/s4dyqL5OvB6OXB6Ek36IFRgQMsiXfqY+lfGK6AIEIa/2pDm+laNssw/y2DVSR6GlO8TFM3ugF3tZmqOFBj7h9iRZ5K/V2KFkXkjW/muNuGYmVR4Ku5Vlt9AwDi+UkChZAII005NTVYQrlsZNipmJICrXZbCq2lo3uog6sny8aLZWNDplIICAUhSlGFSC4rlojSK8pSy0sjCrLeFuZoVjAFAFxPi/tQlHu+91jnmmUafg0UCYVvsnNwHA91c3VkqB009e3tE4uLzTVY+oAgqH6od/1Q70nLBz6/EXnl30cJ79M1O0Tcj5nWcd20NUN7jmlj2SPKpPFJ5aezBHeUlVm8mAOCufOJMNOO0sSN3vVD8aZyWUpM6e7Ysv2DvJ8SjOUGlkii3xdv5EkTyATbYO6C2NL9ibwTmfwwf85oX9ZQomnVZP5DBqtE/CqqcK5mzqA3e5IrY7C59yHAtFCn/RF54wWyZGWLAuMn83Y3dzpstbEC8L4s6m1pJABgRYMFDVja07cMYdaSjyYtY1U8LQoHADRi1acSahQIiuH4nKfXtaxVL8+AsfWatnPx0rneoSFEGkLkUumBNvYAgJ9BKwWGPcxOOZ0Y9ShbnbSYLSifcO/c+T5jrRiqWvecjope++gxDAEcBxiOL+nYYUKzys0Efy43rbTM6k9EiEinwaGdBoe+exx3YuvN+Hdq56xYINmx4GTk84TZm0dTGeb9VqVo6f3sscrXtpRGQbZz7KlqjUx7akh39zNZgruJvJMMoqtBa4XniV4k8I4Vid8pb9XK8Q864uJMaxdRuDJX+ORjyV/l0qSWjuv130HjhzXrZ/h2KRG//FAwUzNpwIUxMMB2gWV3I8KcUKeDb/MnK20WBAg+bL06ohZQICnal3oMANDKLqS6tbIAoUL0quQtAKCDfesa3up4/If7WcmqQ1cGa1u7vuY6zhEY7uHh38PD/3V+1tKXYSp5+ERe8YpX93Z10Fv31AIa2zkpU3OYJLI7g2PFO1fnTlLSzvCX+wYNbOvpgQPwMCVlSdhdTw6nk483AMCDw47Os8TfZyIhneqHdKr/6NKbg2uvlOTzVO2PL79Njs5afWy6q4+D6XcjE2xtyQ2JMMOfM8aZpkNfBAKwB6OXB6OX4VI3ucJnL/LnAQAIEKW+zeRAzgSlgB0RZrR1+jOt4kp06S4vlpEtLxVmh5wt/fvG+kO6peMNX7XhsNlXWZGowvma1sqB1qWR/ToLMgNUkAn2LZxPKJeTAbYL7Kg1NQQqiqWlG+J2VMj5XLLdT97WKTJ2O/++DJPbk+1a2NZISBrD8b0x6grPznTWuV5ja7LN19LJ/VKfHzQl/a6lxirt1/lLb46dUtcQu3bzw8GjlaXelv9+KUpXYfezSdG+Rzd7Hdnkd2zLpZTKSiXb2vdN/3Fx+o+LP46Z68qoXaGYg2/fLe/cqb2XJwxBBAjq7uf3W+dOu15WuqWdmcxcPt/wHWpO5yHNDzxb0XtcFROTnVIwr+/WqBd6RQp138r1UAeXPTqtVVUMfY641GASzPJjj+ztcam+zSQtuU1v1qD+nncdqS1NHJL2DGvE0iPpuaWaLQQC3L1Fvd+n91YeKlBUgaJaVw1c8G9esXb5BhJCmDGs3djeIcqr5Artq+oSb85PiaXblK8d6d2D7LdCoKb7vkSY1dz5cJnkgx1Vr2a+uaQI0rcm/M2TVzARxtLAOU+KwvUFVZkOXyG4lXcfANDHuTsMwXfyHx1JP230Kp3ElBTkCdUfuZUtutTcBHDIlANdhnS8tF8lwHArI2FWUGsMwzXjV1EUR9HKb3KFAlPoejsl80pQHAcAKDAstZruYB3wqaCwnVeVEN8uPj7L71WWxebS6MVCoa7rrAyVQZm9ZXS7fsFbZh3lFVf+vfg84fLRu5fsndi2T7CJ94Ehs1dgBIisFU1KglkDvB9UL5lT/SkEiELQcMAjEKV6YKr2gM798aNWy8hlR6tbKC2ubvupeuPW4w9fxaQrDdYXx5s9GcNlyWV/OTP6Neb+YRVVZQAADJGtZa1QHLuWe/tS9k0FruCS7ZYGznGhOkXytAvTWsCJjPNiVEKA4I72FjpfVSTx1CrSTBJZq2CqxXCp9L5egec+7znGlxZa5bZWx49jZzgcH4YgrVAsBY4RPstI2FIpFVIrJCeYSLOO9f++t/SPaQc/RVQ6ChVyxYapBxfs/KHLUAuTNOSYIFMQViB6WS5LkaAlKCYhwBQa4sgh1XOitXVjdOvhruP71YC10mSA133Nw76et6r3MWJBw17GpeWUrJpiVhp3JSiGE5GvKKXelzPDid6TTvSuyUqwlnhfFnU683K2OBcAEMxpNMN3IovIBPqDqkx3ukeXxz4tegkAQHHscdGL3k5dezp17umkTgE7ln72dr7u6hvVEWioUHlY1R/kqSFDrHrKkRMvjpx4odnt1LnKBenwwXVXqFXF+MBm4wMNrambODuHJSaNC1YHQN2KT2jqUqklTSeRBFJLZLwsxs6JvfHCnF2/nrp3tvL3hqHY1tnHCARCx0FmzyTS+dcji7fIsSqTRAUmqpClVcjSMgVhUSXbm3EXuzH0qsKLFPm3MvqbGG5KJtg05S52Z3TXbDRksIp5wp2nn47oFtzA20gWsU4EYimTVithNV1HbF+3aGDrUN3ClfuOP41Pzt++ZoRW+8lLrw+eemFgZTqgR5Nff+5h1khWbL7GoJMXz7TEoAMAUByLKH13K+9+siANAMBA6GM8hnZyaGuZ3HV1SmW83ckHAQD2ZLsiacm5rCvBnEbOFB2yRybC1IiTKhALDPQ0lyKx+mPA+lx+dfyYNpPGV8pSX7z6rqREMHVSRwDAkpUXqt/ha+DnVi2mXb4qUSg6+3jjOHiYkvLXy1dnRo1UnqUgiFAur+MhIUTC/B0/OLjZntxWOWHBMXzr7KN0FiW0S0PT75MluPOmcLXS0e7G6Moi+ZJhDgLT5RhfgpbypHGZgrv5ohcvC5a0BhsN2CzTkaJlaRWXTTVYArF09tZLZXzRyB7aGV43n8fefB4LAJg6uM1Pg6osiIYuOjSqR7Ph3YIBAMlZxX3a1kohYoVChx9N4yymUOgw4ZExWZ3b1lsxzxK96rNX34Y9+nR4xwTtZ6Eoqj9J1ShLotdki/MAAFQCpYdT5/7OPekIzeK7aSHFpH8m/lMh59MQ2soGC09nXQ4vjtiasHtdo2UWRzbUt1FHyReLhW8Ls0MdTFVMNoBYIb+VnqA6bGTrCABwdGT9vuGayu8OQ9Cs6dbMKKoN2nh4/DNowNI7d/94/AQA4M5m/ztkUEPHyr05hECQoyj+JWb44xb2pdDIB9dWSlEq5OjayQe2Xpnv38TUDZNPpfsBgNq7/K3lICcTbMgEGzbJ15PZL0f4KDz/15jSPfoMFg1xGub7xpTHKTDR5bT2clzb5afbYBWW8n/ddY1Np3Rs5jdj44UNP/cN8lfv4/Rt12DVlF66H4NiSsUlFMXySyoa+VoyNaslMBxQKRbWKC3ni4m1kJwVwPSTYrLujp06O7RjIHQr3hnF0T8T96YI0iAATfMZzyXbTfQanchPyRXn/5m4Z1G92UTYkoiWQFsHNwY7+3PZqxWv7p3tNaaG4elyDJ3/7Gbh5/kaDEF9vQIBAJ3aB3a6rbu0GhEhIF+Tt0GTdp6eT6dOyeLxAADuHI6mbfqynohhP3cDAKhslkwiX/Pjvl1hi2wdTarRJ1Rk0xFnw9t5rvTOdKKLQJ4FQE3NMgLr/ubW8a59HZOxct/tFg09Vv7Uk0CA910Kn7rhbK/W9X8e1tbB1tQoZAIBfrR3lmaLgXmZBSz744qBs8ENLU9I1klmTqmbM8e69wQATPYeC1tUl4hMIEMAohB0WwoZJt+euDea9wkAMNpjiDKUgYHQ5wdMX/VpU0x5/PbEf+YFTCfCZptvCIDpjVouf3VXeRhXWjgq7NTODv0tLuycXF6yNDzsTUG2qmWUfxOjO49rV5od7luXQAB46AlnvzHhhy9otob93K28hH9hT6VvuySf9/vE/VuvzjNF4IFB9ODL0kskUXYUvSmKBeLXInkek1SLbuIqAy0o5e84/eTJu+SfBrWe2L+lcnNjxtC27YN9/jz5+MzdD7NHaRcIMh0D8zIL2LB0UPuWutNldx9+HJ+sIzwPhoBYYokHAcPxj3E5LqZ9EZmFTmulwBVRvE8feB8zRdmFkmIxKkZxlEKg2JJs3KkujdkNQm2DnSgO+mQeBArhtoQ98fwkAEAvpy79XdT+NW+658++k3YlHfjA+/h77NYF9WZyiGYHJYwNbPokJ/Xe59jR2NLC3tcOD/drPCqgiYFKhVrgALzOzzyf9PFK6idlIIKSJlzn5S2qpLwuXnH+h9FtGjWokqq2bPXF4YObNzV5OVM3xJb+mcI7ojokQOS2LsfY5PoPs/p1dr+m3Cmrb6+deV4ieZsnuNeIu/Rx9pBObpdqe5CTlg8qyS9/dKlyXZbwIf3QuqtT1xgXd2xgM/VVwZLHudO8mP1c6B1ZRB8SgYVANDkulKJlPGl8tvBhjuAhAKCx7Syjd7OYKgbrnwsvxBL58d/H+bpV+cJs5Ot8aOVoDNcRz5qeVzpiyRHNlu2nHm8/9VizxagabN0Q1MDt8Jnwu09i9XVoEey1bfXw6u3vojKEImlCSkHkpyyrz920CC95cyrjYomsVKtdqBAJFaIsUU54yZsj6ae7O3Ya7j6QVG2KlCnK3pawp1BaDADo69x9nKf2j9PKLpQnrziafiZZkHYr794YD5NkSDWBANjRof+osNMfSyq/FRQYdjox6nRilBuD3dLRvam9iyeL48Zgs0lUKoKQCYgMQ0VyeYlEmCUoTysvfVeUE1GQXSzWdk/4se0OdxtGQ6r8UBiGz5p/ovowhg7UscmlWYcipbxUhqJ1Kf3ewHZ+A9v5qsNPJZsF8gw2uT6KSwCOK+cc7wp+zRVWVpSxpQS3dTmG4QoMKAAAWLWCC0bJ5Z/EcKkba5LOs9kVh4iwjSNj8NvcfqEuN5SNEATN3TY2MzE/JaYy8vbKgUfNOgYadcC7M7qjuCSyeEtqxeXUiss6+1AIdiH2y1zolk9rjFLFYK2c0tNAsSPlKYRA0AxW8HK2jTg6X98lKn7debUGg9QxEgNzJYlUDsM6foofhrX6YZh6KfrLb2f8fRxmT9YhYaHFiYuv+/cIYtAp63fePrx9AqOqnPbthzG3H8YAACaPbvvjyBoFOl3Ivn4x+zoAwJFi357bKpAV4EJxVPq2yuTlZTJeqjDjdcm7BH7yjby70eWxqxospFV10u9PPV4oLYYANNJj8EAX3fPZXk5dCBDhdObFHo6dLBsnnUg632fsylf3zlVVa8gWlGcLyi+mWBI7Nty/8ZqW3bWslZK/to1t3LCKa1/fLiGNqL78dkbC7eMJOrtZi85uvoe76ZWUwXG0etxjiOOWELAFACDHBI+zBpr4oJyKY8mlG8DnaAAYooa6XqciHhguV6YlAwBeZIYqMGXwNuTKGudnuwLD5RiuAABgWBWdLxKFuPLw1Jnd/hCUK9Uo8T/nnTjwbCWdZUQ1wYvZ35XeOVMQli96WSFLlqAlKCYlwBQqwcGGHKiMwzKaAm06MEQyEjiqaa2yC3nn70dGJeYWlPL5Iqk9h+7pbNuzdeCaab1ItSxpaJQmDd3X77i1dvtNnWcRAjxumNVCz28/jElMLVg5vx+HRf2UkDt/9fltq4YxNfJIe3dptGx275o/KIGfrLRWA117D3cbSKi6WnQgcx3I3HpMv95OXaPLY3cl7c8UZR9MP/WLX5WQ3b7O3f5NPfGL/0/BBiUZujt2bGvXnFaDHUkKAdnctndPjwCL9bBUhDq4zW7SpoOrXs2v6nu+OsPcAQB+dautrJpjapLMO0QicDyYQ6RoCQXRm7vHk0ZL0KLrqUEAAE+Wjnm9JmJFVn37rSrN9ZjCGTJFERWpsiJu6/FW+YIvi8ko22X4hg5utr9sHv3HtIPKw7LCiv2rL87703iZAiLM8GUNyxR+zJMoEwnIAMXL5AW5koKEitfj/fuoej7NX5tcUTmX7Ov+jyO1MQDgQtqood6nlKvjyJIj70sOqvIQyQTmWN/bAIDzaSOHeZ+GAKyltKVEt+l5+iFl2e6bnUP9Zo/q4O1iS6eRi8sEEZ8yD197ffj663+WDLdja29pxacXTlh94u7fP7N1JYUjBAKBYLWq9zt+146xqiU+xuVs23tv0cyedjZ0AMDaRQMXr7/404Lj86d1a9nMDFE9U3hSFA4ACGI3GOVuxKMcxG4ww3fi1oTdL4vfTPYeq6lH2tqueVNOkD5nvCY1sVYqurr7dnX3fZabdiUl9lGOXsVRnThQGV3dfQf7NmphsJS0owNr3mLtRCIyCZk1TUeIQydXH1sK1axhWB0cV+C4AgAgUuQySZWhgjfSmsIQsa/3O1W39IpzIQ6bXRi9isSv8oTG02whjY8qbDB80sQgvg4DmkXc+/jgQoTy8O7pl12GtmjSNsCUa/ny7JE+l+j6zTEAoIPTig5OK7QaFbgUx3HlvIgvz+nhutmNrp2Ei+IyVZ/q6PjJFSi27uDdEd2CNV3sLvbsQZ0ad29Zb8r6s7vOPF0zTXtOoUBRHAf6wqP+mGVqQQ6rc+1u1JY9d3WeivyUdf76u+rtO9eObNbY43F4woZdYeOGtuzRsTKajE4j/blq+N7jTw+deWF1g6XU6qvHMsnf14gdCADAAV4q49GoVWbyplgr69Lexbu9izeK45FFuTElBcnlxcm8knyRQCiXCRUyiUJOJiB0IolBJDnRmAE23AAOt7Gdc2OukykfrIVzei2cY+peDZ1IOtVz9M7IF+8Kc0qlIrMEiy1AMyqtOu1dT6le9/P+oJkNVib9KJClOjt013WdhUTk9BDL0wEAyiWhKZdMXzf87aPY8pLKgJK9y8/vvr8UNm1icTZ1iFYLl1J/gMcBAEAq//7jvNXVLxnufb5am9mTGB0Gq7RcyOOLOzTzrX6KTiW1D/Z59iGl+qk6wIDp0cfOtSMH9GgyoIfZWrEymeLvw4+nj+8wpE+VuFkSCZk9uYvO/YcawiXZAQDShSZVH1BGxsMQbEeqqZ6ktSBAUIiDa4hDHany6yPQxv6fzoNM6fkgf2NHh3kIbB37nl5x9mOxWtEpunid8oUzXdsqSdHi9wWLmjluup/VS6IoACYsCY2CAxTFhB29tMUYEkt+Syz5TWdJZwAAg02bumbolllHK3+E+NywU+F9fjCqzQAAAAZmWD7Mbj5MvZHuR5I6whDxR/9HBm5uoI8Og2XHptswqW8+ZQYHaL/5pDLF65iMAE8zVHUMwCD6EWCasg4NmWBPJhjRGtdnen5eeiqovtv08dbcmyCRkHP7pup03oOqzj6jkBF7CsFRghYAABCYSdNTtKKjfZsHhU/flkbezLvXx7mbgYl9iiBtT/IhAEB7bqsaqvH9d8gUvnlR+DdfUeBCDeritJiG2OE4qkxqiyw9F170j2ZnN1rIAHeTCj6q8GKN9GKN1HnqXqb60yuQp70tWNDAboENOai7xz0AgIlLQiPguhOMA+zWOzOHR2TrNR9dhra4ffxFzOvKIJXjm290G96SZCy+mkl0qT7DggA81u8WCWYAABS4JKLw7wzhUynKZxAd67OHNrSpNMo/+j+BKyebkHJ7tDoafbTRYbAIBPi3yT1+23OzmCfs176hh5MNjUIsrRC9j88+efstiuGzR+owDQiBAACQynSPQCcUxKmb51vT+9ec3AJe2KNPkTFZGTmlfIGEAMNMBsXP2z64oXufro04rCpuHaW1kivQbiO2b189olmQjqgfU3xzBIja0cPQ94kSf6bPULf+F7Ovn8g4/6zoZVtuy0CmvyPFnoZQcRzwFfxyeUWqMONtaWQU7xMOcG+65wSvUeb89P9p3pQc6e26jkV0zhRGvC4+2NlJXYI42HZEsG0Vr+id3FWWPSWTf0mG8vw4VeIMCBAFQBAAAMMV7wuXBHGX21KMqJI161g/O6UAAOARUBnahmt8tnV8ziGAA9Sy+PKfVg6e23eL8jVCJEjEMqMGq7Pz2s7OAACQUnGnTJYaytWufJMtfFUhz+7vcYBGsCuTpd7PWeLL6kEhVIlktCX7PchZqpUIPcDjoOFH6/bedWjqe2rd+LP3Pmw6er+oTCAUy+w4dA8nm7G9Q7u2CCDpyorwcrH1drUbtNDQ8+aM6vgF1WbuPY3b+HdY+5Z+Y4e09PWyZ7OoKIoVlwoSUgou3fxw4uLrbauG1ffXDn3EMRzDcH0p02sXmaqUaArD3Po7URxOZ17MEGVnZGbr60aGSd0dO41wgELTrgAABEJJREFUH2hBqHp1lD4vipVWRl8/KK4wULlWicVaaSqPuyZd3CtjoGAI6eB61pT7rD9TJfaSgjjHFS2IK6qMHyJAVD/bZVoDRmDOk3S12g+L3MSOZlIGcr1mXu37N3t+80O/CR1+XDqAxjQ0Zw8v3BbP0w7Cii49qXptRw4Y6HnIjd4qV/j2esYUKcZnII6NbEYqrRUCkaHPq5OGNiMa2oxQGizN6SEBIkH6VzB6txvcHTkLx5lUh1oJhYSc3aCdG/xVsfPfB8P6NpsxoaOqhYgQ3Jxt3JxturYLnLfq3L+nXmxbZaRSU23TjtuylV1IZFlMJC8mU5RdIC0SoxIMR8kEih3Jxp3q0ohdv7ltUysmHo5wHzTCfZDqkAAjCIRUD0n91mnJnXwr57dyWbYrrWlX56VaZ3my7PiKsFbcn8JyV/Zy+b0Gz4FwYH2hSjfWJH3RoSqau94CAP/8+YcAgDPL9Za81OKXzaPGLeyjms0ZoI3DgjYOxoXFEYjSxnFhG8eFWu3DvM9otZRJ0yJLDhdKPkpRPg3hejDaDvDYb0A/67+i6Q4AwDHcgOWGIUiPw6quQSAk1DY41Db4izydDJOOt9xjvN+3hhut2RjvY7dzlvd2XVf9LA4wHMcAADiOKTApmaBdu9hE6ETPmJI/Esr+0Wr3Zo9pZLdE5yUwhCjDFOCalX0HAAAAWTY3ZHLoTI4ZX4EStDyq9FiuMIKvyMNxjIZwHaiNg20nsElqt0lC+TUJymtiO17zwnNpw4Z7n1PZI6Gi8F7Or03tJrd2nE+CmQJ5flLFzdvZcwZ4HDTDh/X/yvxp3dfvulVcKujdpaGPpz2LQcEwvJQnjE/Ov3TrQ1Ja4bZVOjZrlC4qs3xz3/mmQWByR0fjyRs64VJb9PXWW2lVJ3aUUDtKKADAgkRCGCIC/WJ4MERUqg/DsJV3Zu5kz/NiduzmupmO2EMQLFaU5Ije3syaOdz7HPGzxjEOsHfF+98V79e6VjPGqkj8yYkWHMCuVHxik9xDudMzBM+EigImUXdx4v8Hg0VECETEeEBH1/aB9Xwdr96J2nf8aU4eTyCSIgiBQSf7edqHNvFcvbC/DVtHLCWBALcI9lqx6aqBUIaZEzuNGqi75vh3vgbCi/ZGlZ4HAMcBviehMwCARXR2ojYEAITlrEwVPFN2+1B6GgCg7AAAmOR3lUKo3aIVNcSFOdbAWdUqUpVIaFVg6HMyBgQIEICr185pZjc52G6iZsuZ1CrZSPbUhhHFu1P499xoLYgwXaQoSq4IQyAyg6hXlgrCayGk6Dvf+drAAQYBSHMTzbpxWP8p1EtCeR4GUBrB1pEWHGz7I5ukDtnJEDx9mLtcaxOQhnBH+VzW/CuUSBMjS44USmJkKJ+GcN3p7Zra/Ugm6FVG+W6wvvOd73wzWC2/7zvf+c53apvvBus73/nON8N3g/Wd73znm+F/kiwShPjIwFQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x300 at 0x2344EC65160>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc = WordCloud(font_path='C:/Windows/Fonts/NanumGothic.ttf',\n",
    "               background_color='white',\n",
    "               max_words=100,\n",
    "               width=400, height=300)\n",
    "cloud = wc.generate_from_frequencies(count_dic)\n",
    "cloud.to_image()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "identified-resistance",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>단어</th>\n",
       "      <th>빈도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>방역</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>대책</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>수사</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>당</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>기업</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>검찰</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>위기</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>정권</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>백신</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>한국</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1765</th>\n",
       "      <td>코로나</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>文</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>경제</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>선거</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>국회</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>與</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>정책</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>필요</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>의혹</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>부동산</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>재난</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>정치</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>한</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>시장</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>규제</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>이</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>여당</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>장관</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>북한</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       단어   빈도\n",
       "729    방역  212\n",
       "539    대책  208\n",
       "1033   수사  180\n",
       "478     당  177\n",
       "386    기업  176\n",
       "180    검찰  163\n",
       "1255   위기  158\n",
       "1511   정권  153\n",
       "745    백신  151\n",
       "1888   한국  150\n",
       "1765  코로나  144\n",
       "72      文  141\n",
       "204    경제  140\n",
       "942    선거  138\n",
       "327    국회  136\n",
       "84      與  136\n",
       "1528   정책  132\n",
       "1872   필요  125\n",
       "1316   의혹  124\n",
       "809   부동산  119\n",
       "1435   재난  118\n",
       "1529   정치  117\n",
       "1884    한  116\n",
       "25      4  113\n",
       "1071   시장  112\n",
       "343    규제  112\n",
       "1320    이  111\n",
       "1166   여당  109\n",
       "1423   장관  107\n",
       "828    북한  103"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_count.sort_values('빈도', ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nominated-contest",
   "metadata": {},
   "source": [
    "- 보수 신문사 단어구름"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "single-feelings",
   "metadata": {},
   "outputs": [],
   "source": [
    "right = df[df['성향']==1]\n",
    "right.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "physical-favor",
   "metadata": {},
   "outputs": [],
   "source": [
    "tdm2 = cv.fit_transform(right.loc[:,'기사'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "impossible-functionality",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_count = pd.DataFrame({\n",
    "    '단어': cv.get_feature_names(),\n",
    "    '빈도': tdm2.sum(axis=0).flat\n",
    "})\n",
    "\n",
    "word_count.sort_values('빈도', ascending=False)\n",
    "count_dic = word_count.set_index('단어')['빈도'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "political-tamil",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x300 at 0x23381150C70>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc = WordCloud(font_path='C:/Windows/Fonts/NanumGothic.ttf',\n",
    "               background_color='white',\n",
    "               max_words=100,\n",
    "               width=400, height=300)\n",
    "cloud = wc.generate_from_frequencies(count_dic)\n",
    "cloud.to_image()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "uniform-storage",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>단어</th>\n",
       "      <th>빈도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>文</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>정권</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>與</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>기업</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>수사</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>방역</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>대책</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>선거</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>경제</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>北</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>한국</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>정책</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>검찰</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>부동산</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>위기</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>백신</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>정치</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>규제</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>코로나</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>여당</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>의혹</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>당</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>靑</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>장관</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>한</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>나라</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>국회</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>이</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>명</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       단어   빈도\n",
       "86      文  139\n",
       "1502   정권  139\n",
       "103     與  136\n",
       "393    기업  135\n",
       "1057   수사  108\n",
       "749    방역  103\n",
       "545    대책  102\n",
       "963    선거   98\n",
       "230    경제   97\n",
       "75      北   95\n",
       "1884   한국   94\n",
       "1521   정책   89\n",
       "198    검찰   85\n",
       "819   부동산   85\n",
       "1262   위기   84\n",
       "760    백신   81\n",
       "1523   정치   79\n",
       "351    규제   77\n",
       "1766  코로나   77\n",
       "1181   여당   76\n",
       "1315   의혹   72\n",
       "488     당   70\n",
       "111     靑   68\n",
       "1418   장관   67\n",
       "1879    한   66\n",
       "424    나라   62\n",
       "339    국회   60\n",
       "1317    이   58\n",
       "647     명   58\n",
       "28      4   56"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_count.sort_values('빈도', ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iraqi-humanitarian",
   "metadata": {},
   "source": [
    "- 진보 신문사 단어구름"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "typical-chance",
   "metadata": {},
   "outputs": [],
   "source": [
    "left = df[df['성향']==0]\n",
    "left.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "foster-burns",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tdm3 = cv.fit_transform(left.loc[:,'기사'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "individual-pattern",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_count = pd.DataFrame({\n",
    "    '단어': cv.get_feature_names(),\n",
    "    '빈도': tdm3.sum(axis=0).flat\n",
    "})\n",
    "\n",
    "word_count.sort_values('빈도', ascending=False)\n",
    "count_dic = word_count.set_index('단어')['빈도'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "acknowledged-perry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x300 at 0x233D5E82E80>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc = WordCloud(font_path='C:/Windows/Fonts/NanumGothic.ttf',\n",
    "               background_color='white',\n",
    "               max_words=100,\n",
    "               width=400, height=300)\n",
    "cloud = wc.generate_from_frequencies(count_dic)\n",
    "cloud.to_image()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "conscious-nerve",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>단어</th>\n",
       "      <th>빈도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>방역</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>당</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>대책</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>필요</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>검찰</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>국회</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>미국</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>위기</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>북한</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>수사</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>백신</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>민주당</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1777</th>\n",
       "      <td>코로나</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>중국</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>재난</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>광화문</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>문</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>의</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>대응</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>시장</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>이유</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>속</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>한국</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>계기</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>이</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>문제</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>의혹</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>확산</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>우려</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       단어   빈도\n",
       "748    방역  109\n",
       "471     당  107\n",
       "555    대책  106\n",
       "1872   필요   90\n",
       "173    검찰   78\n",
       "324    국회   76\n",
       "698    미국   76\n",
       "1285   위기   74\n",
       "844    북한   73\n",
       "1053   수사   72\n",
       "767    백신   70\n",
       "713   민주당   69\n",
       "1777  코로나   67\n",
       "1611   중국   65\n",
       "1440   재난   64\n",
       "291   광화문   63\n",
       "686     문   63\n",
       "1324    의   61\n",
       "550    대응   59\n",
       "1094   시장   59\n",
       "1357   이유   58\n",
       "19      4   57\n",
       "1041    속   57\n",
       "1888   한국   56\n",
       "218    계기   55\n",
       "1343    이   53\n",
       "688    문제   53\n",
       "1339   의혹   52\n",
       "1960   확산   50\n",
       "1263   우려   50"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_count.sort_values('빈도', ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "under-bathroom",
   "metadata": {},
   "source": [
    "# <font color='CC3D3D'>감성 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "fifteen-yeast",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tdm1\n",
    "y = df['성향'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "impressed-timer",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "blond-coral",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "integral-bowling",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "212/212 [==============================] - 1s 3ms/step - loss: 0.6906 - accuracy: 0.5370\n",
      "Epoch 2/3\n",
      "212/212 [==============================] - 1s 3ms/step - loss: 0.6675 - accuracy: 0.6770\n",
      "Epoch 3/3\n",
      "212/212 [==============================] - 1s 3ms/step - loss: 0.6486 - accuracy: 0.7262\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x233c79decd0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train.A, y_train, epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "brazilian-sherman",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53/53 [==============================] - 0s 2ms/step - loss: 0.6524 - accuracy: 0.6673\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6523794531822205, 0.6672576665878296]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test.A, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "manufactured-relaxation",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>토큰</th>\n",
       "      <th>가중치</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>괴물</td>\n",
       "      <td>-0.319028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>필요성</td>\n",
       "      <td>-0.220127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>분노</td>\n",
       "      <td>-0.197984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>해결</td>\n",
       "      <td>-0.194457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>巨與</td>\n",
       "      <td>-0.187081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>계속</td>\n",
       "      <td>-0.187041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>수사권</td>\n",
       "      <td>-0.179908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>한심</td>\n",
       "      <td>-0.169354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>행위</td>\n",
       "      <td>-0.165257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1155</th>\n",
       "      <td>언행</td>\n",
       "      <td>-0.163001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>실질</td>\n",
       "      <td>-0.161981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1766</th>\n",
       "      <td>침해</td>\n",
       "      <td>-0.160743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>적</td>\n",
       "      <td>-0.156408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>산업부</td>\n",
       "      <td>-0.156100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>협상</td>\n",
       "      <td>-0.155418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>감염…</td>\n",
       "      <td>-0.154698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>확진자</td>\n",
       "      <td>-0.151809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>당선인</td>\n",
       "      <td>-0.150377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>문화</td>\n",
       "      <td>-0.150143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>최소한</td>\n",
       "      <td>-0.150119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       토큰       가중치\n",
       "291    괴물 -0.319028\n",
       "1872  필요성 -0.220127\n",
       "828    분노 -0.197984\n",
       "1908   해결 -0.194457\n",
       "71     巨與 -0.187081\n",
       "213    계속 -0.187041\n",
       "1044  수사권 -0.179908\n",
       "1897   한심 -0.169354\n",
       "1920   행위 -0.165257\n",
       "1155   언행 -0.163001\n",
       "1107   실질 -0.161981\n",
       "1766   침해 -0.160743\n",
       "1453    적 -0.156408\n",
       "913   산업부 -0.156100\n",
       "1941   협상 -0.155418\n",
       "125   감염… -0.154698\n",
       "1964  확진자 -0.151809\n",
       "495   당선인 -0.150377\n",
       "674    문화 -0.150143\n",
       "1732  최소한 -0.150119"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>토큰</th>\n",
       "      <th>가중치</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>南</td>\n",
       "      <td>0.167239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>與野</td>\n",
       "      <td>0.168951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>에</td>\n",
       "      <td>0.176758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>평가</td>\n",
       "      <td>0.181583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>세경제</td>\n",
       "      <td>0.187221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>일정</td>\n",
       "      <td>0.193228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>배제</td>\n",
       "      <td>0.193602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>野</td>\n",
       "      <td>0.206158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>세월호</td>\n",
       "      <td>0.206819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>’</td>\n",
       "      <td>0.216379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>核</td>\n",
       "      <td>0.220989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>軍</td>\n",
       "      <td>0.235342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>낙관</td>\n",
       "      <td>0.235459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>기재부</td>\n",
       "      <td>0.247730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>親文</td>\n",
       "      <td>0.248632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>秋</td>\n",
       "      <td>0.263154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>정리</td>\n",
       "      <td>0.274257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>前</td>\n",
       "      <td>0.277644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>思見</td>\n",
       "      <td>0.326468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>美·中</td>\n",
       "      <td>0.381188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       토큰       가중치\n",
       "69      南  0.167239\n",
       "87     與野  0.168951\n",
       "1166    에  0.176758\n",
       "1835   평가  0.181583\n",
       "984   세경제  0.187221\n",
       "1384   일정  0.193228\n",
       "751    배제  0.193602\n",
       "90      野  0.206158\n",
       "1000  세월호  0.206819\n",
       "62      ’  0.216379\n",
       "77      核  0.220989\n",
       "89      軍  0.235342\n",
       "414    낙관  0.235459\n",
       "386   기재부  0.247730\n",
       "88     親文  0.248632\n",
       "81      秋  0.263154\n",
       "1511   정리  0.274257\n",
       "66      前  0.277644\n",
       "72     思見  0.326468\n",
       "84    美·中  0.381188"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w, b = model.weights\n",
    "words = cv.get_feature_names()\n",
    "word_sent = pd.DataFrame({'토큰' : words, '가중치' : w.numpy().flat})\n",
    "display(word_sent.sort_values('가중치').head(20))\n",
    "display(word_sent.sort_values('가중치').tail(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "applied-junior",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_coo = x_train.tocoo()\n",
    "index = np.column_stack([x_coo.row, x_coo.col])\n",
    "x_train_sparse = tf.SparseTensor(index, x_coo.data, x_coo.shape)\n",
    "x_train_sparse = tf.sparse.reorder(x_train_sparse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "curious-tribute",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "212/212 [==============================] - 1s 3ms/step - loss: 0.6276 - accuracy: 0.7488\n",
      "Epoch 2/3\n",
      "212/212 [==============================] - 1s 3ms/step - loss: 0.6119 - accuracy: 0.7617\n",
      "Epoch 3/3\n",
      "212/212 [==============================] - 1s 3ms/step - loss: 0.5977 - accuracy: 0.7726\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2356b4619a0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train_sparse, y_train, epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "random-newspaper",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5852 - accuracy: 0.7831 - val_loss: 0.5819 - val_accuracy: 0.7607\n",
      "Epoch 2/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5739 - accuracy: 0.7881 - val_loss: 0.5770 - val_accuracy: 0.7563\n",
      "Epoch 3/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5636 - accuracy: 0.7887 - val_loss: 0.5727 - val_accuracy: 0.7578\n",
      "Epoch 4/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5539 - accuracy: 0.7946 - val_loss: 0.5689 - val_accuracy: 0.7518\n",
      "Epoch 5/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5449 - accuracy: 0.7976 - val_loss: 0.5655 - val_accuracy: 0.7459\n",
      "Epoch 6/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5366 - accuracy: 0.7992 - val_loss: 0.5625 - val_accuracy: 0.7415\n",
      "Epoch 7/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5288 - accuracy: 0.7992 - val_loss: 0.5600 - val_accuracy: 0.7430\n",
      "Epoch 8/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5215 - accuracy: 0.8009 - val_loss: 0.5576 - val_accuracy: 0.7430\n",
      "Epoch 9/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5147 - accuracy: 0.8047 - val_loss: 0.5555 - val_accuracy: 0.7415\n",
      "Epoch 10/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5082 - accuracy: 0.8066 - val_loss: 0.5537 - val_accuracy: 0.7400\n",
      "Epoch 11/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.5021 - accuracy: 0.8065 - val_loss: 0.5523 - val_accuracy: 0.7415\n",
      "Epoch 12/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.4963 - accuracy: 0.8084 - val_loss: 0.5509 - val_accuracy: 0.7386\n",
      "Epoch 13/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.4909 - accuracy: 0.8086 - val_loss: 0.5499 - val_accuracy: 0.7386\n",
      "Epoch 14/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.4857 - accuracy: 0.8119 - val_loss: 0.5491 - val_accuracy: 0.7371\n",
      "Epoch 15/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.4808 - accuracy: 0.8129 - val_loss: 0.5484 - val_accuracy: 0.7326\n",
      "Epoch 16/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.4761 - accuracy: 0.8135 - val_loss: 0.5480 - val_accuracy: 0.7326\n",
      "Epoch 17/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.4716 - accuracy: 0.8147 - val_loss: 0.5477 - val_accuracy: 0.7297\n",
      "Epoch 18/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.4673 - accuracy: 0.8160 - val_loss: 0.5473 - val_accuracy: 0.7297\n",
      "Epoch 19/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.4632 - accuracy: 0.8176 - val_loss: 0.5470 - val_accuracy: 0.7297\n",
      "Epoch 20/100\n",
      "191/191 [==============================] - 1s 3ms/step - loss: 0.4593 - accuracy: 0.8180 - val_loss: 0.5472 - val_accuracy: 0.7267\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2356b584f40>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train.A, y_train, epochs=100, validation_split=0.1, callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "characteristic-durham",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6879432624113475"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp_model = MLPClassifier(random_state=777)\n",
    "\n",
    "pred = mlp_model.fit(x_train, y_train).predict(x_test)\n",
    "accuracy_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "decimal-archives",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6985815602836879"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm_model = SVC(random_state=777, kernel = 'rbf', probability=True)\n",
    "\n",
    "pred = svm_model.fit(x_train, y_train).predict(x_test)\n",
    "accuracy_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "herbal-investing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6595744680851063"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt_model = DecisionTreeClassifier(random_state=777)\n",
    "\n",
    "pred = dt_model.fit(x_train, y_train).predict(x_test)\n",
    "accuracy_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "demanding-coast",
   "metadata": {},
   "source": [
    "# <font color='CC3D3D'>주제 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reserved-cookie",
   "metadata": {},
   "source": [
    "### 文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "trying-hunger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd = TruncatedSVD(n_components = 100, random_state=777)\n",
    "svd.fit_transform(tdm1)\n",
    "word_idx = words.index('與') ;word_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "extensive-mambo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2356bb85fd0>]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotator = Rotator()\n",
    "rot = rotator.fit_transform(svd.components_.T)\n",
    "loading = rot.T\n",
    "plt.plot(loading[:, word_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "excellent-rugby",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>loading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>고집</td>\n",
       "      <td>0.041270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>수도권</td>\n",
       "      <td>0.041854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>협치</td>\n",
       "      <td>0.043357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>김학</td>\n",
       "      <td>0.044707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>개편</td>\n",
       "      <td>0.046625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>민심</td>\n",
       "      <td>0.048206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>참담</td>\n",
       "      <td>0.048842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>미얀마</td>\n",
       "      <td>0.049103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>무능</td>\n",
       "      <td>0.049252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>조선</td>\n",
       "      <td>0.053977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>혈세</td>\n",
       "      <td>0.054961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>아이</td>\n",
       "      <td>0.055114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>집단</td>\n",
       "      <td>0.055668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>강화’</td>\n",
       "      <td>0.058781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>核</td>\n",
       "      <td>0.059566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>기업</td>\n",
       "      <td>0.067496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>불안</td>\n",
       "      <td>0.067967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>검증</td>\n",
       "      <td>0.069436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>비리</td>\n",
       "      <td>0.127827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>軍</td>\n",
       "      <td>0.897666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     word   loading\n",
       "228    고집  0.041270\n",
       "1041  수도권  0.041854\n",
       "1943   협치  0.043357\n",
       "400    김학  0.044707\n",
       "153    개편  0.046625\n",
       "694    민심  0.048206\n",
       "1684   참담  0.048842\n",
       "687   미얀마  0.049103\n",
       "655    무능  0.049252\n",
       "1550   조선  0.053977\n",
       "1936   혈세  0.054961\n",
       "1126   아이  0.055114\n",
       "1662   집단  0.055668\n",
       "138   강화’  0.058781\n",
       "77      核  0.059566\n",
       "383    기업  0.067496\n",
       "845    불안  0.067967\n",
       "177    검증  0.069436\n",
       "861    비리  0.127827\n",
       "89      軍  0.897666"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_idx = loading[:, word_idx].argmax()\n",
    "topic = pd.DataFrame({'word':words, 'loading':loading[topic_idx]})\n",
    "topic.sort_values('loading').tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "declared-desperate",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>與</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>신문사</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>경향신문</th>\n",
       "      <td>0.004219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동아일보</th>\n",
       "      <td>0.024034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>머니투데이</th>\n",
       "      <td>0.004822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>이데일리</th>\n",
       "      <td>0.004877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>조선일보</th>\n",
       "      <td>0.045724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중앙일보</th>\n",
       "      <td>0.008831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>프레시안</th>\n",
       "      <td>0.008374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한겨례</th>\n",
       "      <td>0.003412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한국경제</th>\n",
       "      <td>0.014984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              與\n",
       "신문사            \n",
       "경향신문   0.004219\n",
       "동아일보   0.024034\n",
       "머니투데이  0.004822\n",
       "이데일리   0.004877\n",
       "조선일보   0.045724\n",
       "중앙일보   0.008831\n",
       "프레시안   0.008374\n",
       "한겨례    0.003412\n",
       "한국경제   0.014984"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_topics = svd.transform(tdm1)\n",
    "doc_topics = doc_topics @ rotator.rotation_\n",
    "newspaper_topic = pd.DataFrame({'신문사' : df.신문사, '與' : doc_topics[:, topic_idx]})\n",
    "newspaper_topic.groupby('신문사').agg('mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "likely-portfolio",
   "metadata": {},
   "source": [
    "### 광화문"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "hispanic-pottery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "289"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd = TruncatedSVD(n_components = 100, random_state=777)\n",
    "svd.fit_transform(tdm1)\n",
    "word_idx = words.index('광화문') ;word_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "allied-cheat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2347e7d9df0>]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotator = Rotator()\n",
    "rot = rotator.fit_transform(svd.components_.T)\n",
    "loading = rot.T\n",
    "plt.plot(loading[:, word_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "every-model",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>loading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>野</td>\n",
       "      <td>0.028306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>이건희</td>\n",
       "      <td>0.028546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>통합당</td>\n",
       "      <td>0.029165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>적폐</td>\n",
       "      <td>0.030077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</th>\n",
       "      <td>주</td>\n",
       "      <td>0.030533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>조건</td>\n",
       "      <td>0.032283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>대표</td>\n",
       "      <td>0.032454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1782</th>\n",
       "      <td>탑</td>\n",
       "      <td>0.032917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>위기</td>\n",
       "      <td>0.033124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>기생충</td>\n",
       "      <td>0.033225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>지금</td>\n",
       "      <td>0.033375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>무더기</td>\n",
       "      <td>0.034221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>소비자</td>\n",
       "      <td>0.034820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>변</td>\n",
       "      <td>0.035685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>현실</td>\n",
       "      <td>0.041046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>파행</td>\n",
       "      <td>0.042122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>대출</td>\n",
       "      <td>0.051163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>해명</td>\n",
       "      <td>0.112415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>실세</td>\n",
       "      <td>0.127443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>무차별</td>\n",
       "      <td>0.940393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     word   loading\n",
       "90      野  0.028306\n",
       "1327  이건희  0.028546\n",
       "1796  통합당  0.029165\n",
       "1459   적폐  0.030077\n",
       "1571    주  0.030533\n",
       "1545   조건  0.032283\n",
       "549    대표  0.032454\n",
       "1782    탑  0.032917\n",
       "1261   위기  0.033124\n",
       "379   기생충  0.033225\n",
       "1614   지금  0.033375\n",
       "656   무더기  0.034221\n",
       "1019  소비자  0.034820\n",
       "770     변  0.035685\n",
       "1933   현실  0.041046\n",
       "1822   파행  0.042122\n",
       "545    대출  0.051163\n",
       "1910   해명  0.112415\n",
       "1103   실세  0.127443\n",
       "665   무차별  0.940393"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_idx = loading[:, word_idx].argmax()\n",
    "topic = pd.DataFrame({'word':words, 'loading':loading[topic_idx]})\n",
    "topic.sort_values('loading').tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "alternative-webcam",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>광화문</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>신문사</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>경향신문</th>\n",
       "      <td>0.018268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동아일보</th>\n",
       "      <td>0.007764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>머니투데이</th>\n",
       "      <td>0.002195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>이데일리</th>\n",
       "      <td>0.018273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>조선일보</th>\n",
       "      <td>0.009904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중앙일보</th>\n",
       "      <td>0.004760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>프레시안</th>\n",
       "      <td>0.032524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한겨례</th>\n",
       "      <td>0.009113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한국경제</th>\n",
       "      <td>0.007895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            광화문\n",
       "신문사            \n",
       "경향신문   0.018268\n",
       "동아일보   0.007764\n",
       "머니투데이  0.002195\n",
       "이데일리   0.018273\n",
       "조선일보   0.009904\n",
       "중앙일보   0.004760\n",
       "프레시안   0.032524\n",
       "한겨례    0.009113\n",
       "한국경제   0.007895"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_topics = svd.transform(tdm1)\n",
    "doc_topics = doc_topics @ rotator.rotation_\n",
    "newspaper_topic = pd.DataFrame({'신문사' : df.신문사, '광화문' : doc_topics[:, topic_idx]})\n",
    "newspaper_topic.groupby('신문사').agg('mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intermediate-nelson",
   "metadata": {},
   "source": [
    "### 정권"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "capital-minister",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1506"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd = TruncatedSVD(n_components = 100, random_state=777)\n",
    "svd.fit_transform(tdm1)\n",
    "word_idx = words.index('정권') ;word_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "changed-visitor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2356b73d580>]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotator = Rotator()\n",
    "rot = rotator.fit_transform(svd.components_.T)\n",
    "loading = rot.T\n",
    "plt.plot(loading[:, word_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "typical-employment",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>loading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>이대로</td>\n",
       "      <td>0.062179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>이상직</td>\n",
       "      <td>0.062273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>국정원</td>\n",
       "      <td>0.064701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>실장</td>\n",
       "      <td>0.066514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>중국</td>\n",
       "      <td>0.070273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>경제</td>\n",
       "      <td>0.071253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>보완책</td>\n",
       "      <td>0.072286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>韓美</td>\n",
       "      <td>0.072634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>정권</td>\n",
       "      <td>0.072634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>개각</td>\n",
       "      <td>0.073886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>글로벌</td>\n",
       "      <td>0.086316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1471</th>\n",
       "      <td>전력</td>\n",
       "      <td>0.087314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>불허</td>\n",
       "      <td>0.091630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>대응’</td>\n",
       "      <td>0.108861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>고집</td>\n",
       "      <td>0.115699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>개정</td>\n",
       "      <td>0.116875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>인구감소</td>\n",
       "      <td>0.125201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>투기</td>\n",
       "      <td>0.145928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>뒤</td>\n",
       "      <td>0.209193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>수준</td>\n",
       "      <td>0.616538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      word   loading\n",
       "1331   이대로  0.062179\n",
       "1339   이상직  0.062273\n",
       "319    국정원  0.064701\n",
       "1105    실장  0.066514\n",
       "1594    중국  0.070273\n",
       "202     경제  0.071253\n",
       "785    보완책  0.072286\n",
       "94      韓美  0.072634\n",
       "1506    정권  0.072634\n",
       "140     개각  0.073886\n",
       "353    글로벌  0.086316\n",
       "1471    전력  0.087314\n",
       "849     불허  0.091630\n",
       "539    대응’  0.108861\n",
       "228     고집  0.115699\n",
       "150     개정  0.116875\n",
       "1359  인구감소  0.125201\n",
       "1799    투기  0.145928\n",
       "590      뒤  0.209193\n",
       "1058    수준  0.616538"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_idx = loading[:, word_idx].argmax()\n",
    "topic = pd.DataFrame({'word':words, 'loading':loading[topic_idx]})\n",
    "topic.sort_values('loading').tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "killing-holiday",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>정권</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>신문사</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>경향신문</th>\n",
       "      <td>0.015024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동아일보</th>\n",
       "      <td>0.000333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>머니투데이</th>\n",
       "      <td>0.000906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>이데일리</th>\n",
       "      <td>0.014681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>조선일보</th>\n",
       "      <td>0.002724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중앙일보</th>\n",
       "      <td>0.008871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>프레시안</th>\n",
       "      <td>0.005872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한겨례</th>\n",
       "      <td>0.006144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한국경제</th>\n",
       "      <td>0.003331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             정권\n",
       "신문사            \n",
       "경향신문   0.015024\n",
       "동아일보   0.000333\n",
       "머니투데이  0.000906\n",
       "이데일리   0.014681\n",
       "조선일보   0.002724\n",
       "중앙일보   0.008871\n",
       "프레시안   0.005872\n",
       "한겨례    0.006144\n",
       "한국경제   0.003331"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc_topics = svd.transform(tdm1)\n",
    "doc_topics = doc_topics @ rotator.rotation_\n",
    "newspaper_topic = pd.DataFrame({'신문사' : df.신문사, '정권' : doc_topics[:, topic_idx]})\n",
    "newspaper_topic.groupby('신문사').agg('mean')"
   ]
  }
 ],
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