NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement:\n",
    "    Process the article and extract most frequent words and expain their usage in the article. Test for the sentiment of the article and extract positive words and negative wods in the article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "import re\n",
    "import pandas as pd\n",
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Scraping the content of the article from the website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "page = requests.get('https://hbr.org/2019/12/how-india-plans-to-protect-consumer-data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(page.content,'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = soup.find_all('p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['India looks set to legislate a Personal Data Protection Bill (DPB), which would control the collection, processing, storage, usage, transfer, protection, and disclosure of personal data of Indian residents. There are a number of features of the DPB that will require companies to change their business models, practices, and principles. The issues raised here serve as a primer for what businesses need to keep in mind about India’s new regulation and the increase in data protection regulation around the world.', 'The Indian government looks set to legislate a Personal Data Protection Bill (DPB), which would control the collection, processing, storage, usage, transfer, protection, and disclosure of personal data of Indian residents. Despite its regional nature, DPB is an important development for global managers. The digital economy in India is expected to reach a valuation of $1 trillion dollars by 2022 — and it will attract numerous global players who must comply with DPB.', 'India has followed the EU’s General Data Protection Regulation (GDPR) in allowing global digital companies to conduct business under certain conditions, instead of following the isolationist framework of Chinese regulation that prevents global players like Facebook and Google from operating within its borders. Yet, Indian DPB carries additional provisions beyond the EU regulation. Because India is a nation state, it would treat the data generated by its citizens as a national asset, store and guard it within national boundaries, and reserve the right to use that data to safeguard its defense and strategic interests.', 'There are a number of features of the DPB that will require companies to change their business models, practices, and principles. Many others will add operational costs and complexity. The issues we raise here serve as a primer for what businesses need to keep in mind about India’s new regulation and the increase in data protection regulation around the world. Understanding these issues will help digital companies plan ahead, address future regulations, and decide whether to enter or exit certain markets.', 'Privacy as a fundamental right: In 2017, the Supreme Court of India ruled that privacy is a constitutional right of Indian citizens. Every citizen, however, leaves a visible trail of private data while navigating in the digital world. DPB intends to protect and safeguard citizen’s privacy rights by controlling the collection, security, storage, sale, and exploitation of these data. New regulation would affect the cost-benefit analysis for many digital firms that often lose money in offering free services, but aim to earn profits from the sale and exploitation of customers’ personal data. Many of those digital firms would have to rethink their business models if they can no longer collect, exploit, retain, and sell user data as profitably as before.', 'User consent: DPB requires that a digital company must obtain explicit permission from a user before collecting their personal data. In doing so, it must explain the extent and the purpose of data collection. Explicit permission must also be obtained at each stage of subsequent data processing. Compliance with this provision could be tricky, because digital companies not only collect personal data, they also process that data to create new information that does not belong to the original user. For example, Uber ascertains traffic patterns and Amazon analyzes feedbacks from individual transactions. Furthermore, raw data could be transferred to a third-party data processor for analysis, creating new information in conjunction with the data received from other data collectors. Companies will have to rethink their operating procedures for data tracking and security as well as ascertain whether, when, and how to obtain user permissions. Digital companies now become “data fiduciaries” as defined in the DPB, instead of being mere data collectors, when they assume responsibility for obtaining user permission for both initial collection and subsequent processing of user data.', 'Ownership of personal data: In principle, DPB proposes that the data provider is the owner of their own personal data. While simple in idea, this notion could impose an enormous implementation burden for digital companies. In the physical world, a property owner can ask for return of their property. Companies in the digital world would have to figure out how to comply with this requirement when the user demands erasure or recall of their personal data from a digital company — for example, when a person requests deletion of all of their information after they cease to be a Facebook member. Digital companies would also have to think beyond their own data storage and usage, because they might have sold the data to a third party.', 'Three classes of data: DPB has identified three categories of data from which a principal can be identified: Sensitive data includes information on financials, health, sexual orientation, genetics, transgender status, caste, and religious belief. Critical data includes information that the government stipulates from time to time as extraordinarily important, such as military or national security data. The third is a general category, which is not defined but contains the remaining data. DPB prescribes specific requirements that data fiduciaries must follow for the storage and processing for each data class.', 'All sensitive and critical data must be stored in servers located in India. Sensitive data may be processed outside but must be brought back to India for storage. Critical data cannot be taken out of the country at all. There are no restrictions for general data. Digital companies currently operate in a seamless cyber world, where they mostly store and process their data wherever is economically most efficient. This locational divide proposed by DPB would impose additional costs on digital companies, might lead to subeconomic storage and processing capacities, and might result in what some refer to as “splinternet”or the fragmentation of global digital supply chains.', 'Data sovereignty: DPB reserves the right to access the locally stored data to protect national interests. This implies that DPB would treat citizens’ data as a national asset, no different than control over citizens’ physical properties. In this respect, DPB differs from GDPR, which imposes no locational storage requirements or preferential access to data for protecting national interests. Currently, digital companies practically own the data as long as they can address the privacy concerns and meet the user-acceptance requirements. One implication of the new policy is that when the government demands its citizens’ data, in case of foreign attacks and surveillance, digital companies would have to abide and assist the Indian government’s defense policy.', 'National interests: While placing a large emphasis on citizens’ privacy, DPB disregards privacy rights in certain cases. It states: “All or any of the provisions of this Act shall not apply to any agency of the Government in respect of processing of such personal data…”\\xa0That is, various public sector entities of the government of India will not require individuals’ consent to obtain their personal data when responding to the security of the state, detection of any unlawful activity or fraud, and epidemic and medical emergencies. Those data could be demanded from digital companies. In addition, the government can direct a digital company to provide non-personal or anonymized data for research or planning purpose. Critics argue that these data can be potentially misused by the government for unintended uses such as political surveillance. Others argue that anonymous data can be easily deanonymized. Digital companies may have to change their policies to comply with these requirements. Recall Apple’s refusal to unlock an iPhone for an FBI investigation. It is debatable whether Apple would be able to decline that request under DPB.', 'Verification tag: DPB requires that all digital companies must identify their users and tag them into three categories to reduce trolling\\xa0 (e.g., an anonymous user or a bot trying to incite violence by posting incendiary comments): Users who have verified their registration and display real names; users who have a verified registration but have kept their names anonymous; and users that have not verified registration. This would be a first regulation of its kind in global social media. This implies that digital companies must put in place procedures for collecting and verifying the real identities of their users. Note that Facebook has more that 100 million fake accounts and faces the dilemma of continuing as is, attempt to verify them, or delete those accounts.', 'Compliance and enforcement: DPB proposes steep penalties for noncompliance. In case of a data breach or inaction by the fiduciary upon data breach or a minor violation, the penalties could reach $ 700,000 or 2% of a company’s global revenues, whichever is higher. For major violations, such as data shared without consent, the penalties would double. These penalties, which are based on multinationals’ global income, and potential jail sentences for officers of digital companies, imply that DPB regulations cannot be taken lightly. Its provisions must be complied with in order to do business in India.', 'Taxing digital companies: As we note in a previous article, multinational digital companies can easily transfer their income to tax havens and avoid paying taxes to local governments, with no fear of confiscation of their properties. Physical control over data and fear of enforcement penalties might give the Indian government additional leverage to collect taxes and dues from digital companies. This would lower the likelihood that digital companies can get away with paying little or no taxes to the local governments.', 'Other issues: The DPB applies to all businesses that collect personal data, not just digital businesses. For example, John Deere collects and processes data obtained from its farm equipment. Whether DPB applies to tractors with sensors, whether the collected data belongs to the farmers, and how the benefits of farm data are shared becomes a debatable point.', 'In our view, there is an urgent need for data protection regulation in India, and it is better to have one, even if a bit flawed, than none. This bill is a good first step in providing broad principles for regulation, and the detailed laws and regulations will hopefully continue to be finetuned — as happened for automotive safety standards that have evolved since the beginning of the 19th century.', 'When DPB becomes effective, the principles of data protection regulations would be similar across the EU, California, Canada, and India. A company that learns to comply with the regulations of one jurisdiction can easily comply with the regulations of another. Uniform standards, similar to ISO 9000, would promote global commerce. An orderly digital market would be a win-win for citizens, nations, and multinational corporations.']\n"
     ]
    }
   ],
   "source": [
    "text=[]\n",
    "for i in t:\n",
    "    text.append(i.get_text())\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extracted the text of the article. Now lets see what about is our article and further process it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Article Summary:    \n",
    "    The article is about the Personal data protection bill and its objectives on how to protect data relating to individuals and the types of data involved and the way they are put into use , the security concerns that come with it and regulations on how and where its supposed to be stored. This bill will make sure the information is used fairly , lawfully and transparently following strict rules called  ‘data protection principles’. It applies to all companies processing personal data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we join the whole article into one and then remove any special characters, white or wide spaces using regex and convert the whole text into lower case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tx = ' '.join(str(i) for i in text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'india looks set to legislate a personal data protection bill dpb which would control the collection processing storage usage transfer protection and disclosure of personal data of indian residents there are a number of features of the dpb that will require companies to change their business models practices and principles the issues raised here serve as a primer for what businesses need to keep in mind about indias new regulation and the increase in data protection regulation around the world the indian government looks set to legislate a personal data protection bill dpb which would control the collection processing storage usage transfer protection and disclosure of personal data of indian residents despite its regional nature dpb is an important development for global managers the digital economy in india is expected to reach a valuation of trillion dollars by and it will attract numerous global players who must comply with dpb india has followed the eus general data protection regulation gdpr in allowing global digital companies to conduct business under certain conditions instead of following the isolationist framework of chinese regulation that prevents global players like facebook and google from operating within its borders yet indian dpb carries additional provisions beyond the eu regulation because india is a nation state it would treat the data generated by its citizens as a national asset store and guard it within national boundaries and reserve the right to use that data to safeguard its defense and strategic interests there are a number of features of the dpb that will require companies to change their business models practices and principles many others will add operational costs and complexity the issues we raise here serve as a primer for what businesses need to keep in mind about indias new regulation and the increase in data protection regulation around the world understanding these issues will help digital companies plan ahead address future regulations and decide whether to enter or exit certain markets privacy as a fundamental right in the supreme court of india ruled that privacy is a constitutional right of indian citizens every citizen however leaves a visible trail of private data while navigating in the digital world dpb intends to protect and safeguard citizens privacy rights by controlling the collection security storage sale and exploitation of these data new regulation would affect the costbenefit analysis for many digital firms that often lose money in offering free services but aim to earn profits from the sale and exploitation of customers personal data many of those digital firms would have to rethink their business models if they can no longer collect exploit retain and sell user data as profitably as before user consent dpb requires that a digital company must obtain explicit permission from a user before collecting their personal data in doing so it must explain the extent and the purpose of data collection explicit permission must also be obtained at each stage of subsequent data processing compliance with this provision could be tricky because digital companies not only collect personal data they also process that data to create new information that does not belong to the original user for example uber ascertains traffic patterns and amazon analyzes feedbacks from individual transactions furthermore raw data could be transferred to a thirdparty data processor for analysis creating new information in conjunction with the data received from other data collectors companies will have to rethink their operating procedures for data tracking and security as well as ascertain whether when and how to obtain user permissions digital companies now become data fiduciaries as defined in the dpb instead of being mere data collectors when they assume responsibility for obtaining user permission for both initial collection and subsequent processing of user data ownership of personal data in principle dpb proposes that the data provider is the owner of their own personal data while simple in idea this notion could impose an enormous implementation burden for digital companies in the physical world a property owner can ask for return of their property companies in the digital world would have to figure out how to comply with this requirement when the user demands erasure or recall of their personal data from a digital company for example when a person requests deletion of all of their information after they cease to be a facebook member digital companies would also have to think beyond their own data storage and usage because they might have sold the data to a third party three classes of data dpb has identified three categories of data from which a principal can be identified sensitive data includes information on financials health sexual orientation genetics transgender status caste and religious belief critical data includes information that the government stipulates from time to time as extraordinarily important such as military or national security data the third is a general category which is not defined but contains the remaining data dpb prescribes specific requirements that data fiduciaries must follow for the storage and processing for each data class all sensitive and critical data must be stored in servers located in india sensitive data may be processed outside but must be brought back to india for storage critical data cannot be taken out of the country at all there are no restrictions for general data digital companies currently operate in a seamless cyber world where they mostly store and process their data wherever is economically most efficient this locational divide proposed by dpb would impose additional costs on digital companies might lead to subeconomic storage and processing capacities and might result in what some refer to as splinternetor the fragmentation of global digital supply chains data sovereignty dpb reserves the right to access the locally stored data to protect national interests this implies that dpb would treat citizens data as a national asset no different than control over citizens physical properties in this respect dpb differs from gdpr which imposes no locational storage requirements or preferential access to data for protecting national interests currently digital companies practically own the data as long as they can address the privacy concerns and meet the useracceptance requirements one implication of the new policy is that when the government demands its citizens data in case of foreign attacks and surveillance digital companies would have to abide and assist the indian governments defense policy national interests while placing a large emphasis on citizens privacy dpb disregards privacy rights in certain cases it states all or any of the provisions of this act shall not apply to any agency of the government in respect of processing of such personal data that is various public sector entities of the government of india will not require individuals consent to obtain their personal data when responding to the security of the state detection of any unlawful activity or fraud and epidemic and medical emergencies those data could be demanded from digital companies in addition the government can direct a digital company to provide nonpersonal or anonymized data for research or planning purpose critics argue that these data can be potentially misused by the government for unintended uses such as political surveillance others argue that anonymous data can be easily deanonymized digital companies may have to change their policies to comply with these requirements recall apples refusal to unlock an iphone for an fbi investigation it is debatable whether apple would be able to decline that request under dpb verification tag dpb requires that all digital companies must identify their users and tag them into three categories to reduce trolling eg an anonymous user or a bot trying to incite violence by posting incendiary comments users who have verified their registration and display real names users who have a verified registration but have kept their names anonymous and users that have not verified registration this would be a first regulation of its kind in global social media this implies that digital companies must put in place procedures for collecting and verifying the real identities of their users note that facebook has more that million fake accounts and faces the dilemma of continuing as is attempt to verify them or delete those accounts compliance and enforcement dpb proposes steep penalties for noncompliance in case of a data breach or inaction by the fiduciary upon data breach or a minor violation the penalties could reach or of a companys global revenues whichever is higher for major violations such as data shared without consent the penalties would double these penalties which are based on multinationals global income and potential jail sentences for officers of digital companies imply that dpb regulations cannot be taken lightly its provisions must be complied with in order to do business in india taxing digital companies as we note in a previous article multinational digital companies can easily transfer their income to tax havens and avoid paying taxes to local governments with no fear of confiscation of their properties physical control over data and fear of enforcement penalties might give the indian government additional leverage to collect taxes and dues from digital companies this would lower the likelihood that digital companies can get away with paying little or no taxes to the local governments other issues the dpb applies to all businesses that collect personal data not just digital businesses for example john deere collects and processes data obtained from its farm equipment whether dpb applies to tractors with sensors whether the collected data belongs to the farmers and how the benefits of farm data are shared becomes a debatable point in our view there is an urgent need for data protection regulation in india and it is better to have one even if a bit flawed than none this bill is a good first step in providing broad principles for regulation and the detailed laws and regulations will hopefully continue to be finetuned as happened for automotive safety standards that have evolved since the beginning of the th century when dpb becomes effective the principles of data protection regulations would be similar across the eu california canada and india a company that learns to comply with the regulations of one jurisdiction can easily comply with the regulations of another uniform standards similar to iso would promote global commerce an orderly digital market would be a winwin for citizens nations and multinational corporations'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove special characters and white spaces using 're'\n",
    "stripped_text = re.sub('[^a-zA-Z\\s]','',tx)\n",
    "# removing white spaces\n",
    "stripped_text = re.sub('\\s+',' ',stripped_text)\n",
    "# remove start and end spaces and convert all text to lower case\n",
    "stripped = stripped_text.strip()\n",
    "#convertig to lower case\n",
    "stripped = stripped.lower()\n",
    "stripped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now tokenize our text into sentences, remove all stopwords from them and lemmatize each word. \n",
    "\n",
    "-Tokenzation means splitting entire document into samller units called tokens\n",
    "-Stopwords are nothing but commonly used words which doesnt add meaning to the document.\n",
    "-Lemmatization means extracting the base dictionary word by removing inflectional endings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "txt=sent_tokenize(stripped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove stopwords and lemmatizing at once \n",
    "new_sw=[]\n",
    "corpus=[]\n",
    "\n",
    "wc = WordNetLemmatizer()\n",
    "sw = stopwords.words('english')\n",
    "nsw=[\"not\",\"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn',\n",
    "     \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\",\n",
    "     'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n",
    "\n",
    "for word in sw:\n",
    "    if(word not in nsw):\n",
    "        new_sw.append(word)\n",
    "for i in txt:\n",
    "    if i not in new_sw:\n",
    "        corpus.append(wc.lemmatize(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising words using Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "wc = WordCloud()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f47c97ab88>"
      ]
     },
     "execution_count": 22,
     "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": [
    "# word cloud for all words\n",
    "img = wc.generate(corpus[0])\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This article is about government introducing dpb(data protection bill) for protecting user data in India from digital companies with some regulations, which is why the terms data, dpb, digital companies, regulations, user, personal data, india are mostly used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>CountVect_Scores</th>\n",
       "      <th>Tfidf_Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>data protection regulation</td>\n",
       "      <td>4</td>\n",
       "      <td>0.091074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>to change their</td>\n",
       "      <td>3</td>\n",
       "      <td>0.068305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>of personal data</td>\n",
       "      <td>3</td>\n",
       "      <td>0.068305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>regulation and the</td>\n",
       "      <td>3</td>\n",
       "      <td>0.068305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560</th>\n",
       "      <td>would have to</td>\n",
       "      <td>3</td>\n",
       "      <td>0.068305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>their business models</td>\n",
       "      <td>3</td>\n",
       "      <td>0.068305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>their personal data</td>\n",
       "      <td>3</td>\n",
       "      <td>0.068305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>to comply with</td>\n",
       "      <td>3</td>\n",
       "      <td>0.068305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>bill dpb which</td>\n",
       "      <td>2</td>\n",
       "      <td>0.045537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>here serve as</td>\n",
       "      <td>2</td>\n",
       "      <td>0.045537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Features  CountVect_Scores  Tfidf_Scores\n",
       "379   data protection regulation                 4      0.091074\n",
       "1398             to change their                 3      0.068305\n",
       "924             of personal data                 3      0.068305\n",
       "1117          regulation and the                 3      0.068305\n",
       "1560               would have to                 3      0.068305\n",
       "1337       their business models                 3      0.068305\n",
       "1345         their personal data                 3      0.068305\n",
       "1400              to comply with                 3      0.068305\n",
       "183               bill dpb which                 2      0.045537\n",
       "643                here serve as                 2      0.045537"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3_grams\n",
    "count_vectorizer = CountVectorizer(ngram_range=(3,3)) \n",
    "X1 = count_vectorizer.fit_transform(corpus)  \n",
    "cv_scores = X1.toarray()\n",
    "features = count_vectorizer.get_feature_names()\n",
    "\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(ngram_range=(3,3))\n",
    "X2 = tfidf_vectorizer.fit_transform(corpus) \n",
    "tf_scores = (X2.toarray()) \n",
    "\n",
    "\n",
    "feat3 = []\n",
    "cv3 = []\n",
    "tf3 = []\n",
    "for i,j,k in zip(tf_scores[0],features,cv_scores[0]):\n",
    "    tf3.append(i)\n",
    "    feat3.append(j)\n",
    "    cv3.append(k)\n",
    "ngram_3 = pd.DataFrame()\n",
    "ngram_3['Features'] = feat3\n",
    "ngram_3['CountVect_Scores'] = cv3\n",
    "ngram_3['Tfidf_Scores'] = tf3\n",
    "n3 = ngram_3.sort_values('Tfidf_Scores',ascending = False).head(10)\n",
    "n3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f47a7b9688>"
      ]
     },
     "execution_count": 13,
     "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": [
    "# word cloud for features of tri-grams\n",
    "d = ' '.join(n3['Features'])\n",
    "\n",
    "wc_img = wc.generate(d)\n",
    "plt.imshow(wc_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This article depicts the safer and secure aspects that come into implementation by dpb to all digital companies that feed on user personal data , thus the terms data regulation protection, business models , personal data, bill dpb are used more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Features</th>\n",
       "      <th>CountVect_Scores</th>\n",
       "      <th>Tfidf_Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>digital companies</td>\n",
       "      <td>19</td>\n",
       "      <td>0.344942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>personal data</td>\n",
       "      <td>13</td>\n",
       "      <td>0.236013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>of the</td>\n",
       "      <td>9</td>\n",
       "      <td>0.163393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>data protection</td>\n",
       "      <td>7</td>\n",
       "      <td>0.127084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>have to</td>\n",
       "      <td>6</td>\n",
       "      <td>0.108929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>the government</td>\n",
       "      <td>6</td>\n",
       "      <td>0.108929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>of their</td>\n",
       "      <td>6</td>\n",
       "      <td>0.108929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>of data</td>\n",
       "      <td>5</td>\n",
       "      <td>0.090774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>comply with</td>\n",
       "      <td>5</td>\n",
       "      <td>0.090774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>the data</td>\n",
       "      <td>5</td>\n",
       "      <td>0.090774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Features  CountVect_Scores  Tfidf_Scores\n",
       "373   digital companies                19      0.344942\n",
       "887       personal data                13      0.236013\n",
       "817              of the                 9      0.163393\n",
       "337     data protection                 7      0.127084\n",
       "556             have to                 6      0.108929\n",
       "1136     the government                 6      0.108929\n",
       "818            of their                 6      0.108929\n",
       "800             of data                 5      0.090774\n",
       "276         comply with                 5      0.090774\n",
       "1125           the data                 5      0.090774"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2_grams\n",
    "vectorizer = CountVectorizer(ngram_range=(2,2)) \n",
    "X1 = vectorizer.fit_transform(corpus)  \n",
    "cv_scores = X1.toarray()\n",
    "features = vectorizer.get_feature_names()\n",
    "\n",
    "vectorizer = TfidfVectorizer(ngram_range=(2,2))\n",
    "X2 = vectorizer.fit_transform(corpus) \n",
    "tf_scores = (X2.toarray()) \n",
    "\n",
    "feat2 = []\n",
    "cv2 = []\n",
    "tf2 = []\n",
    "for i,j,k in zip(tf_scores[0],features,cv_scores[0]):\n",
    "    tf2.append(i)\n",
    "    feat2.append(j)\n",
    "    cv2.append(k)\n",
    "ngram_2 = pd.DataFrame()\n",
    "ngram_2['Features'] = feat2\n",
    "ngram_2['CountVect_Scores'] = cv2\n",
    "ngram_2['Tfidf_Scores'] = tf2\n",
    "n2 = ngram_2.sort_values('Tfidf_Scores',ascending = False).head(10)\n",
    "n2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f47a76f788>"
      ]
     },
     "execution_count": 15,
     "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": [
    "# word cloud for features of bi-grams\n",
    "data = ' '.join(n2['Features'])\n",
    "wc_img = wc.generate(data)\n",
    "plt.imshow(wc_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This article is about the digital companies using the personal data to benefit their market  and government bringing out the privacy model and making sure they comply with the regulations. That is why the terms digital companies, personal daata, data protection, government, comply are repeated.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "vader = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "positive sentiment : (compound score >= 0.05)\n",
    "\n",
    "neutral sentiment : (compound score > -0.05) and (compound score < 0.05)\n",
    "\n",
    "negative sentiment : (compound score <= -0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.0, 'neu': 0.956, 'pos': 0.044, 'compound': 0.3818}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.0, 'neu': 0.928, 'pos': 0.072, 'compound': 0.5267}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.0, 'neu': 0.818, 'pos': 0.182, 'compound': 0.9313}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.0, 'neu': 0.902, 'pos': 0.098, 'compound': 0.7506}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.047, 'neu': 0.826, 'pos': 0.128, 'compound': 0.8674}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.009, 'neu': 0.913, 'pos': 0.079, 'compound': 0.8779}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.041, 'neu': 0.938, 'pos': 0.021, 'compound': -0.3976}\n",
      "\n",
      " The above Sentence has Negative Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.018, 'neu': 0.948, 'pos': 0.034, 'compound': 0.1154}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.088, 'neu': 0.876, 'pos': 0.036, 'compound': -0.62}\n",
      "\n",
      " The above Sentence has Negative Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.068, 'neu': 0.801, 'pos': 0.132, 'compound': 0.8034}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.085, 'neu': 0.854, 'pos': 0.061, 'compound': -0.5812}\n",
      "\n",
      " The above Sentence has Negative Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.066, 'neu': 0.9, 'pos': 0.034, 'compound': -0.4854}\n",
      "\n",
      " The above Sentence has Negative Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.102, 'neu': 0.852, 'pos': 0.046, 'compound': -0.782}\n",
      "\n",
      " The above Sentence has Negative Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.106, 'neu': 0.838, 'pos': 0.056, 'compound': -0.5414}\n",
      "\n",
      " The above Sentence has Negative Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.0, 'neu': 0.917, 'pos': 0.083, 'compound': 0.6124}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.039, 'neu': 0.798, 'pos': 0.163, 'compound': 0.8402}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n",
      "\n",
      " Sentiment Score: \n",
      " {'neg': 0.0, 'neu': 0.883, 'pos': 0.117, 'compound': 0.7964}\n",
      "\n",
      " The above Sentence has Positive Sentiment\n"
     ]
    }
   ],
   "source": [
    "pos_senti = []\n",
    "neg_senti = []\n",
    "neu_senti = []\n",
    "for i in text:\n",
    "    sc = vader.polarity_scores(i)\n",
    "    if sc['compound'] >= 0.05:\n",
    "        #print(\"\\n\\nText: \\n\",i)\n",
    "        print('\\n Sentiment Score: \\n',sc)\n",
    "        print('\\n The above Sentence has Positive Sentiment')\n",
    "        pos_senti.append(i)\n",
    "    elif sc['compound'] <= -0.05:\n",
    "        #print(\"\\n\\nText: \\n\",i)\n",
    "        print('\\n Sentiment Score: \\n',sc)\n",
    "        print('\\n The above Sentence has Negative Sentiment')\n",
    "        neg_senti.append(i)\n",
    "    else:\n",
    "        #print(\"\\n\\nText: \\n\",i)\n",
    "        print('\\n Sentiment Score: \\n',sc)\n",
    "        print('\\n The above Sentence has Neutral Sentiment')\n",
    "        neu_senti.append(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Out of 17 documents we got 11 are with positive sentiment and 6 are with negative sentiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'neg': 0.056, 'neu': 0.86, 'pos': 0.085, 'compound': 0.9883}\n"
     ]
    }
   ],
   "source": [
    "senti = vader.polarity_scores(corpus)\n",
    "print(senti)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Overall article has a positive sentiment with 86% of neutral words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = corpus[0].split()\n",
    "pos_senti = []\n",
    "neg_senti = []\n",
    "neu_senti = []\n",
    "for word in words:\n",
    "    sc = vader.polarity_scores(word)\n",
    "    if sc['compound'] >= 0.05:\n",
    "        pos_senti.append(word)\n",
    "    elif sc['compound'] <= -0.05:\n",
    "        neg_senti.append(word)\n",
    "    else:\n",
    "        neu_senti.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 34, 1624)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pos_senti),len(neg_senti),len(neu_senti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f47ca62748>"
      ]
     },
     "execution_count": 46,
     "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": [
    "# word cloud for positive words\n",
    "wc = WordCloud()\n",
    "pos_words = ' '.join(pos_senti)\n",
    "wc_pos = wc.generate(pos_words)\n",
    "plt.imshow(wc_pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This bill has the ability to control misuse of data and considers users priorities and insecurities.\n",
    "So it includes necessary steps to ensure that access to all sorts of personal data will be enclosed to market aimers with severe guidelines that every data using company must abide. Hence the terms , interest, security, consent , certain, important, safeguard, protect are considered as positive words wrt to article."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f47eca5708>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zet8lJFyUTeSQJJcnfH/ol37uKz43dvx8tsLIgAFSiorsrFpl4ttvTZzMs/HPV3R9Pc3zAofRSPU773SoMgSZjNi5c4m76iqiLroIWVwctuZmql5//aypbAzHjrnkojkTQS5HFhdH6pIlJN58s0tSsnbMpaXUfvhhhzFWlZND5IUXEnnBBU7BXlJC1VtvOf3xfWAqKKB82TLMxZ3J96QaDeqhQ1FlZTnVWm3je3L/bLevxE3MQBHj3XjbTpWPG0BPUcZryLrZubk78PhXLoIIwNpq5vAz33W4AUbluhv+Q8Xpt7dT8O4Oj08G+uIGTr22GYDY8elu7f0FZbwGcAZE+rOeaVeM8Nlf05EKjj27yu14yYrO70DCRdkBz7Nf7tyXLXMV3HqdyH/ecnV7e+WVGD791EiY4DEVFFD6178SPX06EcOHI09KwmEwYK2txXDsGC3btuEwm7FUVaFITe2+wyCImjqVuCuvRBIRgehwYKurw9bSAu35WqRSJCoV8sREJEolkZMnYykvp3nLFre+dPv3YyouJuXOO5HFxyPI5Tj0eixVVVS98Qaiw4GluntPJWtNDeUvv4x66FA0o0ejyslB0ebFY2ttxVpbi6mggKZ169yurd2cT8rluQgSgeRZQyn9/IDbOfJoNeAMcKvelB/ginVP2lWjOtxwW/M9P+2KdgcF72xn3F/6tqZObVueJVVi78cL9JS0q5wBiTWbTvm1nuk3jqd85RGv/RW8u6PbqFhtdnzA8+yXwv1MNm5y9wOfe0X3IfBh/Mfe0kLDypU+c6iU/e1v3fbTuGZNUMm82n3aDcePU7diBbbGRo/nCRIJ2S+8AEDklCkehTs4o1jLXvRuN9EfOuQzj3tXjHl5HUZWf6lcfbzj0TvlsmEehXvyTGc+mpotp3sUQ9EdcRP82wXX7y7GbrYhVcpQxmswn4UaAmfS/vcLMkmPPcB6m/b1rN1y2ud57eupzY73up52o5XGfb6fHgHk2sDlXY+FuyAI6cB7QArgAF4XRXGZIAhPA4uB9lva46Io9jzZOLBmdQJffWXi6FEroggjRsh4483e/eCVfrKH0k/29OoYYVxRpju/NMbTp6l6090I1RXR4Wjz8Zf4ncisL6jfXUzJp/vIuGkC0SNSyb5jMoXvdxr/VEmR5P7qUiyNBo56eDQPFolcSuzYgX6dK9ocNB4oJWFKNgkX5VD+be/kKxJkEuLGpxM7diBxF2Qij1IhVcmRKqRIlF1Ekgd1W1/TdT3rd3utkwH4t56Nh8o8GnzPRJAHrkEPZuduAx4WRXGfIAiRwF5BENq3bC+Jotj9Ns9PHn2smYULNcyd67x7FRba+M0j3jMmnmtcEXUXB4zrqbYW9fVUAOd8Tpp2U2Tx/ijZK7Q9mkqUSr9OF9oSh/X3KkiVq4+TcZMzfUHKZbkuwj3l8lwQoGrdSY95kYJFqpYHdL7D5LQRtHt7hJqB88eQs/BCFLHd2x/6I13Xs2ueKG90t54Oc+89mfRYuIuiWAlUtr1uFQThOM7aqSHnk0+MfPJJWL9+vtMeuq8cOJCoqVNp2bbN43mCTEbk5Mkd71t3evaL7i+05teiK6xHmx2PJtM191HKZcMAfHrSBIPd0H3VrK5IVE6RYDOEPiVG7oOXMnD+2I73usJ6ij7Yjbleh63VjN1kxW6yMv2zxSEfO1R0XU+JSt5hMPdGb65nd4RE5y4IQhYwHtgJXAzcJwjCz4A9OHf3nhWnASKTga37m2WYc5SuOeMTbryR6GnTMObnY2tqAkFAGhnpTCqWleVSRKN1T/9Xn1WuPs6QpdNcjmky4zqiLAN1c/MXh82OTWdGpvXvaUgZ5/QEsTSGfjPVIdhFZxKyiu97vX5PyOm6nsq4CKzNvtepN9ezO4J2hRQEQQusAB4URbEFeA0YBIzDubP/u5frlgiCsEcQhG6/mQ/cr2X3riSKi1JJTXXm+75qXtigej7SuGZNRyUjeXIyURdfTNxVVxE3b57Tmyc3t0OwN3z3HdXvv9+X0/WbqrUn3DwikmYEnjq6JzSf8E9tJZFJ0bRlVW3J63m+o+6oXHvCq2CXyKUej/cn2tezPcjIG2drPb0R1M5dEAQ5TsH+gSiKnwOIoljdpf0NwGO6tUAKZC9apGH5cgMPPdTpHvXL+7Ss/M7k46qeI0ilRF11CZoLxyGLi8FSVkXL6i0Ydh3qlfEARNFBjnIsA+VDUEo0mBw6Nutcy7plKkZiEQ1UWl0jJK+IuosTpl0UW466HFvX+gERkkiGKicRLU3E4GjmkHEzekeTS5/pilzUEi1mh55y6ymvc5wYMYcYaSISQYrB0UK5Jd9FL5+pGEmuajInTbsYqMhFLdFgdhiot1Vy1LTVa79dafzhB3S7d6OdOBHVoEHIExOdyb1EEYfZjK2hAUt1Nca8PJ++8P0Nc52Oxv1lxE1IJyo3hZYTVSROaxPuvRw+UL+rmPhJmd2eFz85s8Ogaa7tvTgSX8n9okel+d2P2OZJI1UFZlcIlvb1TJw2mMo1J7ye176ehtLGXl1PbwTjLSMA/wGOi6L4YpfjqW36eIDrgaCtctffUE9hoc1FuOcO6x0vTtWwbJL/31KXY8rBGSQOvg3HXTdQ9qs/I1oC02P6w3DVhWzXf02B+SAAMkHBnKg72axbgdHhHrHoDzMjb2GfYQ17DJ69MNQSLemKYWzXf4VddOq7lEIEg5WuucsVgooZ2p+wrvW/iHQa/caoZ5Aky6DG5poOt95eQZGu80bjvCkk0WTvVD1IJXK06mSa9WVu87LW19O4enXgf7AfyKVqHKIdu8Pd5VAqkTN11C85dPozj/MKloNPfM2ML5Yy/NezKF95hKihzqIjZ0ZihpqST/eBQ2TofZcw/bPFHHziG1q67OZlWiUjfnM5SZc4K0+dGfYfajJvmUjV2hPYurh9SlVych+aReoc3/l+utJ02HmTiBycyKBFUzn9lmcbTagp+XQfQ++dQdKMwX6t5467/UsnHmqCkZAXA3cAhwVBaHfefRy4VRCEcTj3I0XAUs+X+8+4sXIKC12V7UeP9o7yXTVqiNc2iVqFMicd04kCr+f0lEprARaxS9Un0YKAQLIsgyJLz3ST1dYiGmzeqwglyTIotZzoEOwAZtHgdl6KPBupIHcR7M45F3oU7q12VxNLra2ECEmUi3C3O6y9IkC7IzfjSqobj1HT5L7jsjusbD4UWOKoQLAbrdRuySd51jCS2774DrONmo3en5bA6aEhj1Qh0yiQaZSoUqJc2pNnDcNc04rNYMGmt2DTmbHpXQ14JZ/vJ2p4MimX5TL51VtoOlJBy8lqHBYbA68d06GTtzYbvSY5UyVHOeegVSLTKNBkOgNrpCo5yTOHto1vxqa3YKpu6aiX0I7DYkOikKFOjeai9xZSueoY5no92uwEki4ZjDxSRfPRSqJH+hco11XNlX3HZOIvyKD5mFPIKmIjkEUo2P+Ye/rq9vVUJUUi1SiQR3aqeJNnDcOub/s72tbTU5W2qnUnSLksF2W8xmU9lXERxE/OcllPT4nLzgbBeMtswaX0dAdB+bR74rnnohmW65zqggVqhg2V8Y9lPdvNdoc01nsWQQBpnO/2nqJ3eE7UpJH23Ie7xe57B6aRRFN1horHE1FS55f4iqi73NrOFOyesIpmFELfu74JCMRH5VDdGFgeklBSudopFNrD62u3nnbZwXpi7J+u9RmINPr3rnlRDKWNbPvZu64niXD0z6tInjkUQSohZnQaMaNdVSCtp2s5/Mx3GEo9+z9M+2iRx+PyaDWjn5rncqx+dzH7f+uaGvvIn39g1O/mIlE4g6SybrvApb1hbwmHnlrJzG9/4fVvPZP6XUXET84CnIXuo3JTOtrOvMG142s9z1xL8JxM7OifV2GqbiXz5ondrmdfcU5EqC5d2shDD2nR6UR+83Akx45ZWbeuj6oX+RFw0BPseL67y+i5PtEq+rZJyAQ5NrpXMckF5y4kz+xeIMPgCCwdL8Cl4x9FLlWx/9SH1Da7RnymJYwjLjKbpJhcRNFOXXM+x0u+w2Y3EaGKZ1DqJcRGZqGQR3C6YiOFlZtdrh87aAFxkdlIpQrM1lZKqndSXL2dybmLiIxIQSpRMG7wLQAUVG4iv7wzl/6cSU8BAvtP/ddtXhnJF5KRNBm1IhqjuYktR/7Z0TZ15L0UVW8jN/1KtzmfScMe18CXytXedbahRhRFdixazoCrRxF/QSaqpEgEmYS6nUXUbDxF9Y95vVocumZTPjsKPyDjJ+OJm5CBKkmLpclIa14NVT+epHp9YNG/AAce+4qU2bmkXJ5L5JBE5JEqzPV6jOVNHbv43kAURfLf2ErlquMu62nVmWk5WX1W1rM7zgnhvmGjmQ0bncJcIgFH6GM9OrA3+E51aqvtnaLMUi//Cn+Eb0+xizYUQvdeRzbRubMsNIfGoLx+//NIJQriIrPc2oZnzKO4eju7TryJUh7NmJwbyEmdQV7Zaux2M2ZrK4cLVmC2tnLxqPtoNVRR1+xUayjlkcilKg6e/hiTtRWtOgmL1RnJvDdvORKJjEvH/ZZDBZ9R25SHKLoGkKzb9yyXTXjcbU7pSZMZnDaT4yXf0aKvIFozgPSkCyit2e0y753H33Cb85mIDjHgnOH7Hl7R/Ul+oi9pIO/VTT26NtB5e8JQ2siJl3wXpwlkHFF0ZpgMJE6gP6ynv39jMGveL4V7X6b8bfp8NfIBSURMHOVyXLTZqV32LubT/tXSDJQhqgnU2cowi05/WKkgQ0SkoItAbbbXMkg51sVbRi3peYKlAsthJkXMYbv+mw4BLhPcI+lOmHaRJMtELYl0M+5KkOIg8Ci7M/X37ZTW7iG/fD0AOmMtJTW7SI4dQV7Zamx2C3aHhWHpV6BSthVq0KZ3CPcRmVezJ6+zyIXB1KmWsjssHWM6HDaPBlVPc1LINAzPmMfevPepb3HmEtGb6pgz6WlqGk9gtrZ2zFtnrHWbc5gwfUW/FO4rPu90+BdF+NUDWrZutXDypBWrFYYMlbF6de+4QQLUvfphhyukNDYa88kCmr5ci6WovPuLe4jB0cpF2vnUWIuxiGZS5JkUWY64CNMmew0JsoFcoLmSRls1SomaFFlWj8c0OlqJkERxkeYaamwlSJCSJMtwO88qmjls3MRUzXyqrIWYRD0KQYVWEsMR0xaMjtC5ednt3vXPo7KvR6tO5GjR17QYKpmSe4YOuBdykUgk3r8idkfnU5WveYcJ0xf0S+He1yl/Rbud5q9/pPnrntc2DQSbaGWPfhXZylEMkA9FKYnA5NCRZ3KP7yqz5hEvTSNGmYjRoee0+QDDVJM99OofJ027SVcMI0MxArNDT4n1OEOV7oVdamwl1NpKSZQPRCGosIpmDI5WLI6zF3mXGD2YvLK1NOmcT09qZaxLu85Yg0Qix+HwrMpy6j9FBMH/2D2TpQWLTU+0ZkDHzt15vNmjTj1MmP5CvxTuZ9KfU/4ufjiWHRuMHN7b8y/6utblAJw2H+R0m5+7N44a3YOBPLlKrmp526+xiyxH3BKEFZo9ZwM8ZNzos69iy1GXQKp2zgy6ApAI0oCELIDB3Eh89CCqG48hkyoRBNdoxtKa3YwddBOFlZuxWPWolbFIpQpqGp36WFG0YzA1kBY/llZjNXa7pUOt0j4n4Ix5iRRUbGLwgFkYLU0dOvfTFRsCmnuYMGebc0K4T5qkID/f1ZvEaOwfZVcXLIpi9+ZwUjN/GTxgFlnJFyGROL2ARNGOzljL9mP/1+21R4u+Ynjm1Uwf8yssVj3lta7eOyaL0xg+YchPkUhkmC0tFFW5BrYcKfqK4RnzuGDoQk6V/0hF/YGOeeWkzgBg3OBbXOZVUrMTUXQwKG2m01vG0syWwy8HvRZhwvQm50SB7AUL1CxcqGFQjvNeVFho46qr63rVa8ZfHn8hgZNHLKx4N3CXwDBhwoQJEq8Fss8J4d6fSUqV8adXk3j3X01YTK5/xu4t4R19mDBhepWwcO8tthZ5L1x7cVb30Z9hwoQJEwRehfs5oXP//RNRTJokJzLK1QA3a1bo/dwD5YoxvktthQkT5vxiyX1a7n0okupKB9fNqcFo8L03ve1ODXffq2XW5LOb9sYAonkAACAASURBVPecEO4LFqj54ksjzU39b4Ovazk7in9BIUc74wJirrscQa3EYTBhq67DdCyfps+7zyqoyBxAwj23IIuLBhHszS3Y6pqoe/1j7M29k6fnTGTJ8ajH5qLMyUA+IAl5SiKCRILDZMbeqsdWVUvzyg2Y80s6Su6dbaRRWpS5OaiGZCJLSUSZPRBBpUAQJDgsFhx6I7aaenQbd2M6fhp7y9lP5eoy35hIlDnpaC4ajywpDolKhaBWIlGrEK1WHCYLosmMQ2fAWl2H8eAJrGVVWKv6pkylLDkBVW4OykHpyJITUGSkIlEonDVxLRbsLXqM+45hOlWE+UQBDlMfpRnxwqQpCh54JIqrZtYQFy/pVrD3JUGrZQRBKAJaATtgE0VxkiAIccDHQBbOzJALfFVj6k4tM3SojLy84DOrRc2ZRuxtV/f4euPhPGr+/pbLMYkUfnpPDEsfieX6i0qpqbQx9wYtJqPIhu+7L+ItjY5k4LLfuRxrXbedhve/AkA9ZhiJD9yBIPN9H9bvPEjd/33kIhQlkRqSf3s3ivTus+wZDxynZtl7fgtV9eihJD3snkiq+rnXXbJmygcmE3/njSgHuwdHdYe9RUfjRyvRb+vFvO2CQOTMyURfexnS2Kjuz/eCtaqOisdCVjbYK4JCTvrLv0dQha7GqfFwHvqtezHsOxbydNaCTEbUvBlEzZ2OJELd434qn3wZS4n3PPBni4cfj2LKxUoWXOW/1qCXd+5e1TJBV2Jq41JRFMd1GeQxYJ0oikOAdW3ve8z8a3v+oehtbrk7mpvuchUKdhvc8YueZ49UZDizy8lTEkm89/ZuBTuAZspYoudf1vFekEpJ+tVCvwQ7gHrccGJvmtuzCXdB3mU8zZQxpD55X48EOzh30QlLbiZhyc1Bz8sT8pQEUn73C+IWXh+UYG/vK/H+O5BGakI0O3dUuTmkPfebkAp2cN6oE+65lfhFPwlpv4rsgaT+4QFibpgTlGAHSH36PmJunNMrUcj+8MbyeHYeS2XRPVpGjpZztCSN55d1BtFlZst4flks+/NTWbU1mcW/9J4WRKMVeOujeNQRnX/LvPlq9uen8t3GJBbdo0UagoJUvaWWmQ/MbHv9LrABeLSnnS1ZouHCixQcPGBFr+/cWf79xbOjTvDFtbdE8tyjdfz1P8kdxwryLGQM6nk2R3l6CggC8XffFNAXOfqaSzHsPIi1spaYG+cELFSj5l2CfvdhLIU9z7OuyHAKd+3MycTfeUOP++mKZup46t/9AtEcuhB/9ZhhJPziViTq0AXDRUwciSIrjdp/vIel1Hse/Z6gnTaR+EU3OjPn9RL6re5ZP3uKZup44hfd6NfGxC8kEqKvmYUiayC1rywP6WfBH+5f3IBEAvf9OpLJU5X87Cd12Lo85Oh1Dqqr7My/vJYJFyj4w19jOHHMyub1bWqlNrGl1Qr8+/14Ft1Sj9nsPHjxJUqefjaG62bXkpUj448vxCCTweuvBKfyC8UnRQRWC4KwVxCEJW3HkturMbX9TgpmgNIyO7GxEmZequSqq1UdP/2BlAEySgrOKErgAJms5zsMiUqJLCE2YOHsLA84E4DIK6b5PtkL0Vdf2qPr2lFkpBIxcRTxC68Pqp8zSfzl7SEVbEkPLgypYG9HFh9L8u/uQTm4+7J2/hIxYQTxP/9Jrwp2W10jxiO+i4YEQsKSm0Mn2LugHj2U5IcXIVH5V/A7VJiMIga9iM3m/H4b9CIWS+dGs67WwYvPtlBSZOPLTw2cOmFlxKjOjZnRKKLVCry+PJ7qKnuHYAe498FI/vuunuJCGxvXmfhkuZ6bfxr8E2AoVv9iURQrBEFIAtYIguBXguq2G8GSbk8kdF4x+h0HsJRVIU9NRBKpQaqNaPutcR7TRiAoAttxlxVZGT7G9YM2c24Ep44Ft7No3wG3Yz5VjDm/GEGlRHPhWK+CSXPhWBo/+BrhjOc6a1k1uq17kUZpiZg4EllSvMfrI8b5X+bME/IBycTf/ROPj8+i1YY5rxBbbSP2Vj0OowlplBZFeirKoVkIcu8fR/WYYcTeNJfGj4MvfhAxYUT3glIUsZRWOvXQbUY9oe2mq0hPQZE5wOulEpWSpIcXUf3XN4J6CgKnTSZ+8YJu1RHm/GJsNfXY6psQzVYcFguymCgkGrXTcDkgGYkPlZFu466QGbG1l1zQ7Tmi1Yb5VBHm/BIcegOCSok0UoMsPgb1+BE+r1UOzSLhF7dS8493+8zwfiYRGoGFi7XctlCDVAraSAkbf+xSWc0G/3wzjtQBUhbe5FpEZ8gwOeMmKtxUOTKZgM3W878vaOEuimJF2+8aQRC+ACYD1e21VAVBSAVqPFznd4HsUGFv0WE/lo/pWL7XcyRqJemvPeN3n+++0sxvn3UKynk/0ZI9VMGseRoeXRyc8STup/MBcOgM1L6y3MVI2bRiFckPL0KRPdDtOkEmI/raTt27aLJQ98YnGPZ25o9p/PQH4u+6Ae10D3YYaXC7Q0Em87hjq3nxbUwnCrwa7ASVAu3FE4m7Y77XvqPmTsew/zjmvJ7HD8jTkoj3ocO3t+ho+X4T+q37fHrCRM2ZRtTc6V4rc0nUSpIe+BllD/2lx3MFiLl+ts8nDNPx07Ss2oLxQPf5zKUxkUTPvxz1qCHIEuM6G+wOdJvck9T1BOXgTOJ+dp3XdtPJQlpWbsB0/DSi1bOThHJoFtFXX4p6zDCv/ajH5hJz/eV+eYqdDZ59MZZBQ2XMvqgak0nks+9d05ZnD5KxfYuZ7MEyfvNEFM8+5UyVIZGAOkLgn39r5YtPXUtcBiPYIUjhLgiCBpCIotja9noO8Afga2Ah8Fzb76+CGaekOJUTJ6zk5so5ftxKZqaMjz8x8NRToQ/5dwToLbDmax1rvnYKgfZCIk/dH/w8pLFRtKzcSOOn37vPUWeg8plXiJ5/GTHXz3Zrj7rSmSOl5ftNnne6Dgf1//kMS1G5T2EaLA6jmZq/vYn5dGm354omC63rttP64w4S7rkFzZSx7icJAimPL6X+7RXoNu52b++G6GtnEXPDHC8TECm+6//53VfL6i20rN4CgOai8STcfZPbjVEaG0XkpVNoXb8z4LkCRFwwGu1Mzxk/m1asovmb9QH1Z29qpeHdL9yOywckh8QdNm7h9UReOsVjm7Wylor/93e/+jHnFVHzojPxnTwtieTf3o00xt3gHX3tZf1GuGcNklFSZCN1gJT7Ho4kO0fGpi5JZetq7Tz3dDPPPd3M7HkqLpqmZPsWMw4HPLC4gWWvx7H2ByMSicDgYTJkUvj68+Ai3INV4iUDWwRBOAjsAlaKovgDTqE+WxCEU8Dstvc95jePNDHnijpaWx1cMbeOMWOrKSkOvEBEb/D9gUxefC+F6bMjiE8KgYm7Cy1rtvhs12/d57VNtFhp/tb3l9+430st0RB5JNS9+oFfgt0FUaT+zU99GiSj580MeI7S6Eif9oTmr9YF1F9X9Nv30/Dfbzy2xSy4EommZ/VjlR6ezAAcJjMtq92zg/YUa3nwLnqK9FQivdyIHEYzNS+906N+rRU11P37Y6/t6iDViKHidw83kpgs5c0P4tm/28KnH7oXmm9nzXcmXvq/WLIHOffW69eYuP/nDXyyMpHlnydw52ItRlPwyoygdu6iKBYAblssURTrgcvcr+gZu3Y5d9N2O6hUAiaTyD33aNxyvPcFjyyqYsgIJQvviyF7qAJ9q4MTh8wcP2zm7WVNQfVtb/K9m7LVNWKrbXB9xG7DePgkDr3vO7/NS0lBaaQm6OAc/c6DGA8HXhMTnPrYhnc+J+WJez0KcVlyPOpRQwLqP2reJV7tKeb8YpqCEO7gjE3w9BQkUauImjudphWrAu5TGu+5OLq1rOqse4t0R/S1s7zecBveWYGtxnexdl+Yjp/22hZ1xTS/VFKh4sVnPWsLjhy0ctM8z7bB/76j57/vuMqqC0e51nfd+KOJCUNC62HVe+b3EJKc7JxmWbmdMWOcX9CEhP4x9SP7zHyxvIVf/KSS+2+tZOWnrWQMknP3Q7HdXxwCvJX9MwXh+RCszzdA8zfBFToxny7FsN/7l1Zz8QS/+xJUCiIv8V7QpOH9r0NimBNtnp8mI2dP7ZFvuiD3fDNyGPtX1KYsKZ6ISaM8tlkKy9DvDE3tXU+ocnO8Ogf8r9M/JGQ3XH6Z0xtlzRoTL/w1mkV3aTh5MviI1VBw6TwN9z8Rxw+HMnn0uQSiY6W8/2ozt10WnJeEv9hqPBfsDlgd0gWJtmdqhI6xC0qxlgX/qN+6dpvXNvXYXL/70VwwxqtwNR46iaU4NOUTvanJJCqlZxtCN4hGzwVg5EnuT2p9iXbaBK+79uZvN4RkDNNJL0Z0QXB6P4Vx45wQ7n/+i1M98dJLOi6ZWctbb+uZe2Xf5MY4E6tVxG5zVrW3WUTMJhGD3oGu9ezknLGUVXk+HkSotjQ6ssfXAjR+4Fn/HCimY/notngOrAnER92b94ZosVL78vs9mpsn6t9ega3esyoufuH1SDSBRWka9rlXtQJnfpbUP/7qrPt6e6Ord1ZX9DsPunhpBUPzl2u9tsVcN7vPIlf7M+dE4rCRI+Wkp0tRKl3/gV991ff50resMbBljYE3/t7IkJEKps6KYOkjsaRny89Kyt9Q5wIBEIIMlgllDhDzyUK00yYG1Yc3/3nTiQJEW2ifAE2H8zx7uEgkqEYMxrDbcwlDj32dKEC02RFk7oZ6RXoqyY8vpe61D7FW9n12VE+Yemhz8YSl2PtnSlApkKck9Nt16CvOCeH+/XcJVFTYXSLCoH8I93GTVQweoWDu9VqyBssxGEROHDKz6svgDJLefIB7el5ABCncQzmn3kwW5c1eEWyf3twXlYMyAhLuDr0R/fb9nuMRcOYgSv3Tg+g27OpINNefsJSEzkDoMBgRLVavRnFZcli4n8k5IdxvuLGePXv6l3dAO8++nszJw2bef7WJ44cs1FSGRrA5vOhb3emF+K9+9IRrrahxGjs9PHYLMqlXI6Y/hFL4dPbp/WZ0ZtSxPzR/uwHNlLFehZoglRJ52UVI1Cpa1mwNOiI2lATjIeMJh8GE1JtwD4ETwPnGOSHcf3KjGqNRpL7egcPeKcxqavu+iOqV45zFOhKSpZhCWLS7v7m69RWi1Ya9RefRDiCNjcZW69mg7A+hFj7d9SlPS/ba5rW/6joaP/2BuNuv8XmeZup4NFPHYykqR7dxF/odB86OV40XXbe9RRfyXOwOowlpjGd7kCTSexbG/1XOCeFeWmqnsdHhppbpD1x7ayT3PxHHk/fVIggwYqySm++O4v+eb2TFez2PoO1vRQr6Emt5tUfhLh+Q3L1w9yJ8RLs9JME7Z+IwmrHV1Ht0z5PGRCKLi/YaX+CN1jVbaV2zlcjLpxJ78zyfOXgUWQOIy7qeuLbEbbaGZvSb96DbuCvgcf1BkZnm8bg0SkvmO0HFLgZEoMbq/wXOCeFeXWOnoqJ/RKSeyW1LonnxyXq2r3dGpG370UBZsZW7HogJSrjj6H83sr7C2w7Ul5DrOEfp+TFeNPir9gocXyo1IQgPl9a12zAdyyftL7/2+xpZXDTR8y9zpoPef4zafy7v8fie6C9CVQh7y7hxTrhCvvDXaE4cT2H7tiS2be386Q+kpss4ut9V+Bw7YCZlYJD3zX6S7a5fYPd8Y/cW5NMViZdzRC99hgLR6r3vQLOOnom1oobal98LPI5AIiFi4ihSHr8H1fBBQc2hK/78D8L0DefEzv2++4ML4+9NqsttDB+rdMnpPnyskury/hFkdT4gOjzbVgR/Mlh6KWkj2nvPXuPrxuHJrTFQDPuOYdh/nIiJI4madwnKnHS/r1UOzSL50cUYD+fR+MHXQddS9efpKUzfcE78Z1audD7mymTOvMj9ic/ebeHXf4inoc6OAIwYp+S2pdG89pzXkrFhAuTM3PTt+OMp482P3VufocCXAPe1qw8IUcSw5wiGPUdQDs4k+bElAd041KOHovrTgzR9voaW7zb2fB5h9WG/pcfCXRCEYTiLYLeTAzwJxACLgXan08dFUQyqwsKB/cksX27goYe0TLqghspKO999l8C8eX0fpfrJWy188lYLMXFSlCqB3VuMvPPP/vukcS7irciEQ+89817nOZ5jIaRRGqextRfUX74ifO0toS8Nac4vpuTuziLrykEZaGdMQusjnw44c+/HLrgS9cjBVP/trR6thbf/gelkIdXP/jvg/s422SnTSE+YhEKuwWxpYfPRfyKTKpkx6kFkUqd9ZPW+P/TZ/C4e8Uu2HvtXj67tsXAXRfEkMA5AEAQpUA58AdwFvCSKYshKwf/mkSbWrjXz0EOd7k6DcvrXQ0dTQ/80+J4PSL3kurG3+pEV1JvAkkiQRKi6zZzZE7zm5hFF7M3BBbf5g/l0CebTJViKK4i8fCryNN/2KdXIIcRcP5umz1cHPJa9ybPTQG8WCg8VsdpMhqTN4lDhCloMlSjkzjnb7GZ+PPg8akU000f9qo9n2XNCZVC9DDgtimJxiPpz4fRpd8HZ3+04r3wceMBKGM9IYz1XO3IEWWBCFh/6zJ0STYTXnC/25lZnNZezROuPO6j43UvULHsPS5Hv5GjR11yKPDXR5zme8JU2ur8TrRmAwVxPVeNRDOYGmnSuyfbEc9ypIVTC/Rbgwy7v7xME4ZAgCG8JghD0N2jcWHdJfvRoP1O+n8Hoif0jqdO5jkSt9OpuZ+sm3313yJJDnypW7qPPPgmPF0WM+49R+cwrPoteIAjOnOyBdu8lt5FEG4Ekon+4SXpDIYvAZj9/gwWD1m0IgqAArgXaa5S9BvwRZ1z8H4G/A4s8XOd3geznnotmWK5zqgsWqBk2VMY/loVed+kvYy7wnZFQKnUWtw0TPPIBPqI6g9wFKzLSAsr14lefXoJ6oHfSHfiNKKLfvt+5Q/eipomYNBrhrRWhyQ0kCCgHZ2A8dDL4vnqBiYNvJz7K6RI6Z8KTAFQ2HOZwkXsZwjOJUMYxaejPUMg0mK2tlNXto7DKWTUtVptJo66YxOhhjB90MwcLPsNsbWH8oFtZf+gFAFJiRzEqaz4mSzNldfsortne8ZQwKPUS0hMnIZUoqGvueU0GCI23zJXAPlEUqwHafwMIgvAG8K2niwIpkL10aSMPPaRFpxP5zcORHDtmZd26vovgfO3TsMrFF7L4GK+pbwNFkTkgJP14Qjk4I+R9Kny4JVoKe55jP1RU//VN0v78oMfSf4JchnJoFqaj3gvIB4JySGa/Fe4HCj5hcNqlxGmz2JX3DgCi6J/dzO6wcLjwc8xWHTHadEZmXEuroYq6lnwi1ck06oqJ1aZjMDcQox2IwdxAi8GZcyg+ahAjMq5m27HX0KgSGJlxDYIgobBqC2nxY8lOmcbxkpU06cuIj8pBqw48ZUU7oRDut9JFJSMIQqooiu1blOuBoBM6b9hoZsPG/hOO708q3/Uns3p/Iv2U6Gsvo/7tFUH3I0il3gtaB4Bu0260My5wO64aPgjNhWPR7zgY9BjgFGbeMjhaistDNk4w2JtaqPz9Mga86LkYuCo3J2Dhbm9u9eghFH3NLAz7jvWrZGbt2B1WHKIdERG7IzDVjM1uYdjAOagUMQhtWfZitOnUteSTGj+Gsrq9pCdOYtORl5kx6lfoTLUUVm4GYGz2TcikCqaNvK+jvyFpsyiq3kbuwLmcKl9Lef0BAPSmOtIT3D+3/hKUcBcEIQJnAeylXQ7/VRCEcTjVMkVntPnFpo3+GXZmXNJ/U3x+81HfqY36mogJI6h/7wsIMlBINXqoV327wx9PmTb0W/d5FO4A2plTQiZ0fbke6rftD8kYocDW2OI906Yy8HKA+u0HiJo73WNb1JyLfev6z0FGZc3nRNlqWgwVOBw2Lsrt1C5rVQlERqSgN9VhtRkwmBvQqhJpNlQgICCVysmvWE9FvetnThSd35VQmnCDLZBtAOLPOHZHUDMCVnze6Z4mivCrB7Rs3Wrh5EkrVisMGSpj9ereyw0SCl58KvQZB88VJJEaIiaOwrAruNqZkZdP9dpmCKAosulkIdaqOuQpCW5tqtwclEOzMOcV9WSKLmgvGu/xuGi3o99+IOj+Q4UkQu09oVoPEta1rt9J1BXTPPapmTyW1rXbgir72N9IjBrCwYJPAZAIUtTKTp8Rm91CYtSQDs+bZn15h24eQGeoRqtOwmR1dyE1mBuIDEINcyb9y1m8jWXLXH2B9TqR/7zlulN75ZUYPv2074t1hPFMzLWzMOw50mOjp2pYNupRQ7y2672U3/NGy7frib/7Jo9tcT+9lspnXgn6SQMv6RD0W/Zhb+l9/3Z/8ZVX3lYbeGS1rboOw96jnotkSyUk3HMrlU8u63eFvbtDEDz/Pw2WRpTySGRSBYNSL3U5r8VQQUrcSE6VrwegWV+GUt4Zn5NfuZFxOQvQqjaBIKBVJSERJFQ0HKK0bg/D0+fRqCumWV9GrDYzqPn3S+F+Jhs3uX8o5l7hfw3NMGcf+cAUoq+aSfM3PwZ8raCQd6Ss9YS1vNp7wWQv6LbtJ+rqSz3u3hUZacTedCWNH60MeK7teHvKEO12mr9d3+N+ewPtzCle23yVs/NF02eriBg/wuMNTpYYR8K9t1Pz97d61PfZJjf9SgbGj0cicYrH2eOfoElfyu68dwE4WvQV00c9gMWqp6hmGxZrpwq2xVBBYvRQmvTOKl9N+jJUis5CIrXNJzlQ8BEX5i7GITowmOsorNoKQEXdASIUsQwbMBuJRE5NU3DGaKE/OOp35y1TWJDCV1+ZOHrUiijCiBEyqmscPP98L+i1pRIy//MXj03Gw3kh/4BKoyMZuOx3bsctReVUPv3Pbq9XjRxM8iN3ux0vvvMxv8b3lHO7/u0V6Dbu9nmdevRQkh5283B1x+GgacVqmldu8Gs+SQ8uRD1uuNd2S2kllU++3OO0AfF33Yj2Es/6d/3WfTQs/9rvKliCTEbMDbOJunKGVzVHyeIneuxa2P6/sdU1ot+yF8P+485KTz39zkokZL75J69lFJu/WU/TilU96xtnTp2UJ3+JIsOzO6jDYKRl1RZaV28NoNJYJ7K4aFQjBqMem4t6/HAEmYzinz8e/BPXuc1eURQ9WvHPiZ37o481s3Chhrlznbv1wkIbv3mkd/LKSJTeg48EqQRBLuuduqXnKxIJMTfNxdbYjGHPEa9BL4JCjvbiCT4FOw4H9W+tCCofTMMHX6PISvPoYqm5eALqsbm0rNqCbvMer6H14NypR105A1l8jNdzRIs1JJ8VWUIs0dddTvR1l2Nv0dH40UosReVYq+v8EmyyxDjU44cTNftir4LdYTTRunpLUPMUbXZqX36flCd/iTTKvTKSJEJNzPWziZo7HcOeI5iOn/ZuaBYEZHHRyFISkacmEjV7KrJk96euMN45J4T7J58Y+eSTnunX5amJyJITkKhVzmhHtQppfEzHa0mECkGtQhbnPOarOLRqxGAy3vgTjlY9DpMZh8GEw2hCNJqwNTS3vTbjMJpwGM1YSip6pdpPf0a02z1mXExYcjPinTdgLijFWl6NvbEF0W5HGqVFPjAF1bDsbnOdN3/9Y9BudaLFSu2y97y6Akq0EcTcOIeYG2ZjLa/GsOeIsyqWQ0RQKZHFx6DISEWRPdD3QA4Hda+H3ktEGqUlYcnNgFOY2qpqsVbUYK1tQDSYcFisCAoZEoUCRU468rQknzegdhr/+61/uXq6wVbXSM1L75D61H1ez5GoVWinT0I7fRLxC6/HrjPg0BkQzRYEpQJpTCSSCHU4nXCQnPerF3XVTLTTJoa0T0mkxmumwq4Y9hyh9pXQVr7p7whSKc0rNxB91Uz3NoUcVW4OqtycgPs17D1C01frQjBDZz4Ue2MLUl9FlQXBaTcYmBL4AKJI3RufOg3KvYggkyIfmIK8J3PsQuv6neg27wnRrMBSWOase+th934mglKBTKkAP25AYQLjnKjEFObcoumzVbSu3RbSPuv+/XFI0/NW/vFfWIp9J9PqCQ6jmZp/vIt+e//xa++Ohve+DHmfVX/4F+aC88f98VzkvN+5h+kDRJGG5V/TsPxrVMOyifvZdb5zxHjB3tRK44ffoN8ZnL+8x74bmql8ymmwVuXmEHvzvO5VLb76a2qh7KFnQ3oDavp8DZGXXegzP3yPsDvQbd1L0xdrsDcGUefXB7a6Rqr+4MxDLkuMI+b62Wimeo4D6AnmglJ0m3b/rxtTfRIW7mF6FdPJQip+v4zUp+/z6kVxJra6RlrX76B17XZEc+9n7TOdKKDymVdI+tXPUI/J9eqv7oYoYj5VjG7rXvRb94e88Efz1+toXrke9cghREwYiWr0kKDTFDd9vhrdJt/G4lBjq22g7vWPsVbXoZk8pkc3etFkwZRXiOlYPsZDJ7FW1PTCTM8vzglXyDD9D1+ukN7cMOUDklGPHopyUAaylATkqYkIggSHxYKjRYe1spbmbzdgPl3SZwXCJZoIp11gWDbyAUkoctKRKBSIoohotuAwGLHVNKDbvAfTiYKzKiTB6TqrmToeeXIC0rhopNFaJJoIpDGRCBIJSCTOGq5WO9bqWmx1jVir6rAUlWM+VXzW5+sJWWIcysGZKHMGoho1FKk2AkGp6PBEsze14DCasdU1YK2sxVZVh377gV4tan4Oc267QoY5P7CWV/e691DiL29DNTyHiieWYe9BvneH3oBh7xHiFs5HNJsp/cXToZ9kENibW2n5flNfTyMobLUN2Gobzim7xLlI2KAaps/QTBlDwj03h7ZTQcBhtvbZzj9MmAs+/iUT3vGrVEWv4pdwb6uoVCMIwpEux+IEQVgjCMKptt+xbccFQRBeFgQhv60a04TemnyYc5vY26/xWkKvp9S+8gHlDz9/VmqVhulbhj52dUjPO9/wVy3zDvAK8F6XY48B60RRPN9BSQAAIABJREFUfE4QhMfa3j+Ks3jHkLafKTgrM3lPZhECBIlA/KUjSL9zBvIoNYJMik1vwtqop+Dv36PL68MKOGHciJw1Bc30iUijtUijtWS++2xHW/Gi37l4QERefhFxd1xL5VOvELtgLoqcdARBwFpZQ+XTnVXhNReOJeEXt3S8L/3FMzgMnkPcJSolyb9b6iyJJwjYW3SY80uoe+0jn/OO++k1RM6eSuuabTR9tpqByx7HVt9ExeMvuZ2b9uxDyOJjKFnylN/r4i/jl77odmz/v38d8nH6M4JEIHp894m1/D3vfMQv4S6K4iZBELLOODwfmNn2+l1gA07hPh94T3RaancIghBzRgGPkCJRyhn+lwVEjnJ1Y5NHRyCPjkCQu0dLhulbrBW16DbsRpmTjrWqjpbvuuiQHZ7VKSmPL8F4JJ/WNVuRRGpQjxnm0m48nEf1c28Q99NrkQ/07o0hjdSQ/LulOPRGWtZsA1FEMTAF9cjBPucce8u8DsHesPwbAPQ7DqCdORnlkEzMpzprwyuyBiBPS0K3ZV93S9EjGk7tRabSoNDGoooNXYrYvkY7LJWkK0aTePlIBEHAVN5IzdojVKzY06FmS7l6HElXjCYiKxGJUsbU1b916WP7lX9DtDv8Pq/ruFGj01GlRHsctx2pWkHajZNIWzDFuclo0tN6vIKStzdjqvRefSz73stIvW4ilV/uo/DVtR3HEy8bScrV41ANjMNhsqIvqKF27VHqNwdfwSoYg2pyu8AWRbFSEIT2wowDgK7RC2Vtx3pFuA+8fWqHYG85VII+vxqH2YZUo0SZGIXueOgDVcIEh+lEAZwoIH7RDdibWrtNUgZQ89K7mI4XdB44I1GXQ2/EdLwAW2OzT+Eee/vVyFMTKV7omn5AkHnYBLR9r2NuuoKoK6e7CHaA1nU70M6cjPaSC1yEu/Zipz93oGmJ/aX4xw+c46QNZsg19/bKGH3BgAWTiZmUTeWXe5FIJcRdPISsJZciVSsofd+ZOdFY2kD1dwcRpBJyHpjD6Zd+cOlDbEsx7e95Xcdt2lNE067THscF54Zx1Iu3ok6Pp+yjHSBCRHYC0eOzsL7kPeFa1pJLPQr2AQumkHn3JehOVVH93UFUqdHETMjCWFLf58LdG57S47ltxwIpkO2LhMs7c0gfe+RDH2eGOZdxEezQI4OpRKVEM3kM1ir3pHOizd3NzmG2EHPDbKKvnknr6q00fOBaDthSUom5oBTN5NE0Lv/GmYNGKiFiylhs9U2YTgSWlvh/nYJ/rsFutDoN4kDpB9uY8O4Skq8c0yFkmw+W0HywBIlCRs4Dc6j+3nOAm7/n+TsuQPYvZqFOj6f4Pxsp/3hnx3FBJvXw+en8fKb95AIqv9xL4auu6TMSZ43A2mzk8APLO54iBKkESYi0DcF4y1QLgpAKzrqpQHtUQRnQtUrwQMAtSbQoiq+LojjJm4+mP8giVSjinfkrRC+P82HCtCPPSAWpBPOJgu5PxpkpMXr+LHQbdrkJ9nZ0P+5EUCrQXDQOAPWoIUijtei37gt77ASItcnQIWAB7AYLhoJaFPEhjtANZNy2J0SpWkH8JbkYyxpcBDt43hjYTc7+MhZOo/ILd8EOYGnSI9MoiRzeGdwn2h0d1wZLMDv3r4GFwHNtv7/qcvw+QRA+wmlIbe4tfbsiofOfbqn7361ZGsY/hLYvqiD3nX2yHWX2QBqWf0PcT69BUKuoe9X9yVC3eS+KzDTi7rwO3fb9JN53O+ZTxTStWBPSuYcCVWwKg668G3lEFA67FVNjNY2n9lF71HuqX01SJgkjL0aTko08IgqroQWrrpGW0hM0FR7G3Oy5jnHqBfOIzhqJMspZhdPcXEfeV//EYfVcjUmmVTLqpduJyEyg8F9rMde2YjdYkGqUnnUBIaLruA078qn54bDLuIJEQLSLRI1JR5BKaNh2yq9+FXFapq56BASBHde4G8ABjj32CYp4LQNunsLwP96I6BAp/3gn5Z/uCsnGwC/hLgjChziNpwmCIJQBT+EU6p8IgvBzoARor2H2HTAPyAcMwF1Bz7KNyBEDSL76/7d33uFxFefifmerere6ZcmS3LtxwzbGGNsU25SYllBCIKGmQX43JBBISLiXEiC5l5YECD3g0Ak2YAwYsI17k4tsyZJl9V53tXV+f+yqrHdXZbXSruTzPs8+0s6cOec7s2e+M/PNzPfNRJcQSfi4ZNShXcF89YlRzP/0125lvlv5iNfzaaJDSbpwOjFzs4kYn4q1xYjxVB0N2wqoWr8Pey9v0IhxKcSenUvkpDQiJ6ch7RJbmwljSR1N+09S9nrPzrPG/vJCEi+Y1iWjgLgFuYy6YDphWaNQ6TSYa5ppPlBCzcY8DEXBGxDcV4SXIBeDgbm0EqREnzO694Nx+Itp2bgVXXoyEefOwVJaRdOH7pGlWr7YTuTyswmbMRGh09I6SLb2gZAw6WzSF13u2BFsMaHS6AlPyiQ8KZOGE/uxGt07RwkTFzD6nLV0aFdpt6GPikcfFU9Eag7a8GhKt7znVi5sVAbJs84HwG41g1ARGp/KhLW/ovDj5zA1u8cXTrl0NmFjEih/ZycVH3RNREvr4PqO6X7d4r91Rcw6/bo2g8MNhi6ud0+X4OjpH39sPZoIPTm/uohjD33o8ThzXStFz2zi5Aubmf70DYy5eQna2DAXWXylr6tlrvGStczDsRK4YyBCeSMsK5GEZZP9cq6EZZPJvH05moiu4Bza2HC0seFETcsgZe1cjj/0AS2H3P2Hh+cmk333RYRljXJJF4BKp3GcY3oGxpI66vsyMSJAHaJj3P2XET0r0yVLGxNGeG4ybQVVI06525pa0CQnIDQapHXwA6DY24wY9hwmbLb7MyTUaq/b2+tf+QBNSgIxl5+PpbQSw57DLvmW8mra84sInTkRabFi2H5wUOT3laiMSYxe/D1AkPfaH7C0NSGEioi0XEYv+h5jV/6I4x8+hbR33b9Kqyft7EsBQfWBzVTt+wJreyvasCgiUrKJyZpK7WH3zos2LJLsi26mpewYZVs/wFhfAQjCk8Yw7tKfkbXyR+S/84TLtQBCxzgCcXSfSBRqFaGj4zzek+xjbN7ejuvrdQ1FNSAlkRNTHb35XszA5rpWaj4/BEDW7edjKKqh9I1tXo+3m6zsv+NlZj5/E0kXTBs65R4sNO8/SeHj6zu/a2PCyLjpXACszUZO/qNvFZK0ZhZZdyzv/G48WUv91uPo4iOIOSsLbVwEuvgIJj58FTtWP+5W3lzTTEhalwMnY2k9DVuPA5LQjARizhqL0KjIvWc1B0rqMJ7sOWqUNjaC3HtWEzU9AyQYimtor2gkJCWGsMwEpITGnX2zEw8n2r47QNTKhST99scYDxxDqFW+mTPUKvTZGahC9Wicm6LC503H1tSC3djuMhlb/8qH6DJSSf7dbZgKSpAWiyNS0dRcTt3+R4+nlzYbNf/3Oim/v4OEW66i4o/PYCl1daPQ+sV24m68DMPuQz6FkBs8BOlnXwII6o/vxtLWBICUdlpK8yn4z7NM/sHviBs3h7qj33WW0kfFo9I4zFcVuz7pNKdY2ppoKNhDQ4HnZZ7Js1eiCYmgcMPzSFvHC1vSVlWMlHZC41KIzZlF/THXFVIdZlX9qChanFN0aVfNQxPpOVaytNqx1Leh0qqxW7z7nOntuL5e19raTv3WAuIW5pJ21TxK/9VVV0KjAoRH2ztA88FTZNywGENxbZdZR4A+MRpTVVOXrDbp+PhprmZYKXdjaT3G0vrO7yGpsZ3K3dZuoeazvvWYMm9xDDjsFhsnntxA7aZDnXkqnYbM288n8cLpqHQaVCFaN/OMpdFAxb93IO12ajYdwnTa+tbQjHgmPXI12rgIUi6fw4knN/QoT9ZPVxA1PYPGHYUUP/O5y3pZbWw4kZPSsLYEk8LwD41vf4q0WAifN43o1UuRJrNPyl0VFkryvbe4pMX98NLO/7sve7Q1NlPxwFMk/+5WIpfNBymxNTZj2O3aGz8de0sb1U++QsrvbiPxFzdQ9qtHXfINu/JIuO1q2gZpbbuvhCdmoI92jDCr97t3fsytDQDET5jrotzNLQ1Iux2hUjFqymKq9m7Cw6I3F4RQEZvj2JDepdi7aKs6SURyFjFZU9yUe82mw6RcdhZZd56PLjGK6OkZRE1JpzmvlKgpnl0x1351hMl/vobGXUUItQptdCiFf/2sX8d1v25IehyRE1O9XvfEUxsJy04k48Zz0ESFYjfbCEmOJuasLPLufgNDsedOXP6D7zPtqevJ/fXFHPz5axiKaxEqFbNfvYXW/AraCqow17USv2QiIakxLit0BsKw9goZkhrLjH86VlOaqpvZe92zvZYJHR3P9OdvxtJkYM/3n/H6tk1aPZOsO1fQerScvJ+/2m/Z1KE65rz/S6TVzvaLH/N4TIfN3W6xsfe6Z7E0DDzM2VDhi1fIkUzolFz0E8fS+G/fA0z3l+7r3L3tUE2ZcyHJs5Zjaq7j8L8e8nhMx47Xg6/cj9XY3W2DIGHSfJJnr0QbFoW022kuOUL1wc20lhe4nScyfTw5F9/iln461vZWDr58f6/HKfQJxStkBx027aZdRV4VO0DrEccQLTw3GZVOg93cP5uwzeiYgBEaVa9Dx6Y9xcNKsSuchkpF9CXnUfN08O6z8G3iWlJ7eBt1+TuJyZpG5nk/IDpzMtGZk2ksOsjJL153TJh6uIa13fvzbDP5Fg9ZoX+ccco9YnwKAMaSnu3glkYD4Jhc0caGu9jG+k0vDavteKXv51YIDEIQe9WFSJOZkCm56HMygsJX+ulYDA6ZNKEROKb8PQ+Spd2GzWTwnGez0lCwh7aqYhKnLSFh8kJisqZiXbCGU9+83Xlc917/wZd/57d7UPCNM065a2PCABh94xJG37ikT2U0kSEelbvQqIieMYbIqRnEzM5EExWKKkTrsNXr+l611malJzPsEIKwedNQR4Vjq2+i4a2e51UCRVtlMQAqjY7QhFSMtZ7dcRhqSntdWWJuqad0y3tYDM2kzr2Y2OwZLsrdWF/hWGap1fdwFoWh4oxT7uowHx48lXvPO2nVTNKvXYg2NnzAMg32Wt7BwHjw2BlpW+/Ebqfslw8HWopeMdaVYawtIzQhjaTp51G8yXX+SBfpWPV1+gRnT5iaHKNehxLvGg1Iu43643tImLQAlUaL3eqfnZYKvnHGKXdTVRMRE1MpfuZzKj/wbbNJ52YpCYWPr/e6SsfTpioFhYEQmzOLmLHTUetC0IZ3+cLPWXUrNnM7NnM7TcWHaCrueiaPvvM4sTkzGXPuNcy85QnsFhNCrUGoHD5MDr3+IOZW1xVfo6YsIn3h5Z3f7TYrKnWXujC3NlLw0dOcbuY59c2/qcn7huk3ed88eOSth2lvVGKgDjZnnHJvPVZJ/LkTCRub2PvBvVD7xSGvit1fzn8UFLoTmZZLTNZUD+njOv+3mQwuyh2goWAvhuoSclbdhjY8GrvVQnvDKRoK97kpdoCGwv2o9WFEpuWijx6FRh+GzWykvaGa5pJD1OR9i83seXlue0MlpVveJSZrGiFxKah1IdhMBhpPHKCx6ADtXlwWKPiXM065N+0pApYSuyAH1dP9XwXTnZZD3t0JR0xK8/m8wcbMqTru+1UsK84NpbLaxoefGHjwsQaamoefOSmQ+KMeSza/Rcnmt3y6vqm5jkNv/KlPx1qNLVTu/ozK3e7rxvtCTd631OS5+qxRCw2L4q/moLaNerObL8Ee6Szb/EW/y/qT226M4smH4n0q+9q6Vm7+xdC92M64GKodW/i10WGkX79oQOfqmJx1QwjSrlkwoHMHC5Mn6Pjig1QuXh6GVisYnabhjpui+M8byaiVwUmfUeqxC7v0vCxYq9KjFj07dfNWVsGdM065g8OPA0DqFfPI+tlKj5OiqhAtMXPGEpY5yi2vg1HLp7hN0KpCtGT/6iKiZ2b6VeZA8as7ogkNcZ9QnjNLz4qlXl5uCm4o9Qg2aWVz7Ws0Wqo85k+MXEyCzvNu1N7KKrjTq1lGCPEisAqollJOcaY9BqwGzEAhcKOUstEZiu8I0OGF5zsp5a2DIPeA2LHmcaKmjmbs3ReRdPEMki6e4fXY/T9+3i3t2B/fJ+fXq9CnxDDnvV+45TftLWbn5X9hzrvuecONled5Vzwrloay4XPPa6MVXBlIPS5Ovq5P1/imsv87qftDuCaGcRHziNOmoRZaTPY2ThoOUmzYj1boWZb4I76qfZXc8DkkhWRz0nCA4607OstfkHQbALsb11Nj6opcNS/uUqI0CaiFltSQXAAK23b3qawnuY61fkexYX9n/piwqYyPWIBVWmix1nK45RvarN5D4vVEq8FOa5udiPDg7xf3xeb+Eu7BsTcCv5FSWoUQjwC/wRE/FaBQSuldWwYJzQdPsf+mfxB/zgRi5+cQMy8blU6DzWjBXNOM4UQ1TftOYixxd09a/20+B4trSL78LKJnZKIbFYm1yUDr8SrqvjpC3eYjAbijwaGhyUZcrOcHua5eGSL3lYHUY7nhqMv3sZFzqDeV0mqpwy7tRGjjqDYOvmO5ebGX0WKtZW/TJ7Tb2ojUxGG2u06qzoheQXn7Mcoa87FL17mEjdXPszzxZrfz7mr4GJVQs2zUjexv2ki16SQSW5/K6lXhbnLpVV0v0jhdKuMjFvBt3ZvoVKHE69Ix2XzfDf7qW628+lYrqclqEuLUJMSrSYhTER+nJj5OzfhcLeeeHcKohMDb2npV7p6CY0spu8+yfAes9a9YfaO9vKFHf+29IW12ar88TO2XPTuN8oSxtJ6i/+15sqk32U48uaFXp2KBZvsuE9mZnu2gX3wTWGdm/31fHFMndfn0f/c/bfzzjeAM2jKQeixsdl2DbrWbOdm63yVtetxKygyD3amQ7Gn8BJt0rF832Nw39tWbyygx5Hkp7Xni2CYtnXl2aes8f1/Kjg6b1KNcHTZ8g60Zg63Zb2ad8kob5ZWeX8rvvZLEhecH3tTmj7HFj4DuGipLCLFXCLFZCLHYWyEhxE+EELuEELv8IIPCIPHnp5swtrtvWV/3QRtbdwRWud96YxTLzw3t/IzP6VuEpUDgz3qsbS9xS0sMzfZZtr7SZKnxqHi7M9Q28ShNQo9y1ZpKKDUeYX7c5aSHTkQlAt+jHioGpNyFEPcCVuB1Z1IFkCGlnAncBbwhhIjyVNYfMVQVBp/D+WbOu6Sc9RsNWCyS/AILv/5DPTfeGfhNKGGhQxfFaaD4sx5j9Sluab0pXf/Qu/NWmxz8oCvdET34ywGQSA63fEON6SQ5EXNYFH8VOlXo0AkYQHxe5y6EuAHHROsyZ/QlpJQmwOT8f7cQohAYByi982HM3oNmLr9BWaUwUPxVj5NilhKrT6PFXIMEorQJlLZ5NoX4kyjtKNRCMygKvMP1uBD962+2WOtJCx3fq1yFbbspNhxgcfzVpIWMp8iwb0DyDgd8Uu5CiAtwTKAukVIauqWPAuqllDYhxFggFxh5IYQUAk5cTPCvVhgsPit7OiDX3dXwH2ZEryBWl4IKDSZ7K0Vt+ygxHuq1bG7EXLLDZwMwO+YiJHZarQ1sqVsHOGzq2+vfY2LUYiZELuR463bKjPm9lj3W+h0V7cdd5Dra8m2nTIn6THIj5hKujsFoa6HIsJ+ThgODUT1BR1+WQnoKjv0bQA9sdPpw7ljyeA7woBDCCtiAW6WU9R5PrKAwAJYuPjOG1sFEi7WO3Y3rPeZZpIlPqrwHyzneusNlaaMnGiyVbK37d7/L9iRXtamYalNxj9cdqfRltYyn4NgveDn2HeCdgQqloNAby845c5X7oqQfeEz/tup1j+kKZyZnnG8ZhZHBmazcK41dIe40QkuENoEKQ34PJRTORBTlrjDsyM7UMmb0mfvoFjRvd0sbmnXuCsOJEdtC4uPUzJ+t57qrIkhP1ZCWoiE8TBAaImgzSgwGSUurnVNlVopLrHz0aRs7dptoaAqcp0O9TrB0cSjnLgxhykQds2foiQwXWG3Q1manps7OJ5sM7NhjYsuOdqqqz7wdokmJav76P7555RvJJISMGfA5OtrM7Bl6ViwNdWkzADV1dpc2U1xi4eARM59+MXiRxOJiVcyYouf6qyPIHqMlOlpFbLSK6GgVKgH1DXZq6hwbigqKLOzPM/PSv4JzI9tQIzqWIAVUCCH8IkRUpIprr4hg7Zpw5p8VgqqfCyqkhGOFFr7Z1s7769vYvLUdi8U30Ty5Bv3r35r49R/c55dTktTcfUcMN1wdQWRE34U+cNjMB+vbeOgJ3/xkpKVoKNw92qeybQZJfE6xT2X7ikYjGJ+jZdokHT+4IoLpk3WDtq07bUqJz+4Uhroec6LmuXzXqvRoVSEcqPfNPe/tP4ryuc0A5Bf4p810MGm8jjUXhLH6gjBmTdP3FoLYjeISK+s+aOXvL7dQWj606+7B+w7VQXL5u9vbXqER03OPjVZxfNfoATn0EQLG52gZn6Pl5usiaWy2kzzhZO8F+0h6qnt1CwFHvhtNiL7/G3KmTdIxbZLOZ+UejNx5cxTTJuuYNlnPxHFa9Lrhs1FpqEgOzXH5bpVmdtV+0O/zxEar+PXPY/jFrdG9H9wDp7eZ7/+4mi++6X9vPipSxfVXRfDnBwc2MsvM0PBfP43hl7dG8+w/m/nDow20GQLfiR1qRoRy//5axwPhb09tvijcnjhduYeFCl7/W+KArlNRNbJMMwNt2GcC/lgV09Fm/L1fIEQv2HvA1K8y6aka7ro9muuu7N/ItTe0WsHPfhLNiqVhzFhS6rfzDheGtXIP0QuefTyBay6PGJTzv/ex797jPJGe2mVSiI5S8cFrScw/K2RA5/xkk+JyV6HvDEWb6e+8VXSUitt/5NFLiV+YkKslOUlN5QjrCPXGsFXum95LYeG83hVjRZWNXftMFJywUFZhpc0gCQ8TxESryM7SMnGcjgm5Wrfec0GRhRvv9K99LC3FUd1PP5rATddGejzmyDEL23a2U1Vj48RJC5HhKuJi1Uwcp2XqJB3jsrucY5nMkl/d7+6SuK+UVViJyCgmPk5FfKya+DgVCXFqxmVrO/+fME7LrGn63k/mJzZ+1ftw/qyZemKj3Xt4J09ZOVbom4+VgdiJyyqshKQWkZSodqnH+DgV47K1fq/Hlek/pcVSS6Q2gRZLLWHqaMoMhznS+HWP5fQ6QWNRZp+u8dGnBpc2Y7VJxqRrBqXNHDpqZsXaCj57291nDkC7SfLi6y3sO2jm+AkLjU026hvtWMyS8bk65s3Ws+ycUM5fEurVPl+8N4Mf3lHDm++19lu+4cqwVe69KfaGJjsvvt7CvX/qfYOsXif47V0xXLw8jCkTHS5kXxok17GXrwp3U+ynyqw89lQTH2xo63UFzA+vieTyVeGcvySUr7e2D9iWaLVKqqptXq975SXhvPLswIOJ95XV36/s9ZiN76SweIH77//ex23c82DgNkQPVT3mNXxOWdsRlqX+hK1V/0Il1IwOn9JruWf/nNBjfkebeevdVg4cNvd4rF4nmD9Hz3mLQ/n1z2KAgbWZr7e2s2ufibNmOF6AtXU21n9u4D+fGvh8sxGD0fNzvm1nO9t2tvOX55qYNF7HE3+M49xFnvdA3H1ntKLcg521a9zD4nUgJTz9QjMPPtZAc0vfhocms+SBhxt44OEGJo3XceWl4byybnAegqcecW1gZotk8tmlmPvYc3zpXy289K8W0lI0jIo/c/2rnMk0mCoAh8fDDodZWZGz3Hy8d2ftmnC+v9azKcbXNrN5Szubt7Tz1nttfmkzTz7bxGvPJXLdbdV8sMHQ79HU4XwzF19TSdupLI/5UyfqmDFFx768nl9cI4Vhp9wjI1T8xUv08dY2Oz+4pXpA624P55v5/SOD9+N3j8ZTW2fjih9V91mxd6eswkpZhT8lUxgu6NVhGKyNGK0tROkSaTCVo1N5Dw7RU5sBuPS6yqBoM++vb2PqolIKinx3X2yzwZbt7V5H9hcsCztjlPuw6/r9/JZoEuI9r3W+5NqqQd1Q4U8MRsmKtZVs2xnYgBcKw4/EEEfPtKb9BFNilzEmYjqtVu9zLz21GZNZBk2bsdkYkGLv4K77vNfFnFlDN38UaHpV7kKIF4UQ1UKIvG5pvxdClAkh9jk/F3XL+40QokAIkS+EWOlPYa+9MoL77o7xmLdleztbtg8PRbnpayNx2cUczj8zehAK/iW/aQsABc07+KbyVU627mdr1Zsej+2tzURnFg+WmAFj/yEzDzzc4DHvTPJJ1Jee+0vABR7Sn5RSznB+1gMIISYBVwOTnWWeEcJ/ca16Wr71u//x/GMGI395zj32pILCYDBS2kx/2b7H81r7EL3w+/6VYKVX5S6l/Bro6xKES4A3pZQmKWURUADMHYB8nYSHCRbN92xH23vQHPB4nn3lWKGFzzcHxzBYYXgSo/O8ZPB0Rkqb8YXyCu9uBzp85Yx0BmJzv1MIccBptol1pqUBp7odU+pMc6O/AbIXzQ/xuhX9rWG0vOmzL40EgTsfhWGMqo+h6EZKm/GFnjZSqdSKcu+JZ4FsYAaOoNiPO9M91ZpHVdbfANmzp3ufCOnLxpdg4btdI7e3pDA0mGx925U8UtqMLxi9rIs/k/BJuUspq6SUNimlHfgHXaaXUqC7e7x0oHxgIjqYOknnMb2hyT6sJia37+6f3w0FhdOJD8no03Ejpc0o+IavAbJTpJQdq6wvAzpW0nwIvCGEeAJIxREgu+fAiX3kHA87EgE+/tQwrMwcp8qG3gWpwshiUswSJsUscUv/pPR/Xb6PlDYDDk+P47IdQVqSEzUkjVITESFIHqUmNFRFSIjD73yIXhAbozpj7Oo94WuA7HOFEDNwmFyKgVsApJSHhBDrgMOAFbhDSjlgbz0ajSAu1vOim8qaM8sZkILCpvInSkEWAAAasElEQVS/9em44dxmVCqYM1PPyvPCWDQvhHPOHpiDvTMRvwbIdh7/EPDQQIQ6nbBQ4dUhUGNj8D+oCgr+xGLvm2lvuLaZB38Tyw/WRnQ62lPwjWFRe/oe1qW2m4bZ+FJBoZ+ohRabHPjOzQ6Ctc1ERqi47+4Yfn7LwIKHKDgYFu4H1D1sgxputkMFhf6yMOn7xHZb264a4L7AYGwzc2bp2fNlmqLY/ciw6Lk39bBmNT5ucGJqKigECyqhIkQTCWbHGgaBCvDdtBJMbWbGFB1ffpja4wSo2SL58c9r+Wqrsc9B4ceM1pC/3be4tiOFYaHcje0SY7v0+AAkDlLAZAWFYKGkNY/pcReQEzUfm93sMKZ76H5vrXb1LzMc2szLzyT2qNj//nIzf3qikephMAkcbAwL5Q5QXmklO1Prlp6ZMWxuQUHBJ0607KTRXE60LgmdKgyBwE7vfteHQ5sZn+MuHzjmBW75ZS1vvT+yd9IOJsHzK/fCyVOeH9SJ4zw/HAoKI4l6Uxn1pjKg7xOsw7nN/Nfv6wek2CPCh8V04qAybGog74jnHXVpKRplyZTCGUbfZkSDvc3kZHl+yezYbeLvLzcP6NzxccNGtQ0aw6YGenKT+8xjPceGVFAYSdhk33Y5B3ubueZ7nt0R3/jTgQemX73SeyjOM4Vho9zLK21eeyLLzw1lrIfhp8LIxFtYQq1W2XLenWBvMzHRntVPeeXAXXQsO0fZ0TpslDvAvz9o85iuUsF9d3mONqMw8mho9DyZGBM1rB7nISGY24zN5vklPdBNVjOm6Jg03rPTtDOJYdUaXn6rBavV8w/vbYinMPJo9LLvIXesMno7nd7azNJFgQs7V98wOC/p3/86tveDzgCGlXKvrLLx5rueeyJCQNaYwE8SKQw+FVWeh+0zpim9tdPprc288syoIZaoi5o6z2vXp00Z2O94wbKwAZUfKfgaIPutbsGxi4UQ+5zpmUIIY7e85/wt8M2/qOEPj3qO/Xhk22jqCjK59cYoVAN4baWlaLjz5iilBxCk/OW5Jo+9UZ1WBNyOHIz01GZGJagD1mb++UaLx/QPX09mXHb/f8c1F4RReXRMv8uNVHwKkC2lvKojODbwDvBut+zCboGzb/WfqF089pT3VQDhYYK/PBTP/q/Tue3GqD6fc8XSUB78TSxff5RKwa7R/PnB+B4j2SgEjjaD5ItvPEe0+uztZObMUn6303nsqSavgWJObzNJiX3bwRoVqWLF0lCf24zdyz4svU7w4evJXjc4nU5aioanHklg3YtJyrxLN/ri8vdrIUSmpzwhhACuBM7zr1g9Y7VKTp6yMma0d/Fzx2p58qF4bro2kn0HzRSVWKiutWFsl8REqYiMUJE0Sk3OWC25Y7U9nmskotEIoiIE6akaoqJUREeqHH+jVKSnapzfBeOyPQ+RQ/SCdS8mUVpupbnF3vVptlNaYXP53txq99qQfeWxpxpZsdTdXpyequHrj1LZ8LmBDzYY2L3fREmpFY0GQkNUxEQ77nHaZB0pSWoy0jTY7XDTz31bftdRj1Eu9ea4RlSEitFpmqCoR6tVcsWPqijZ7z2KU0ebefyP8bzxdqtLm9GoBSlJapc2MzpNM6DePjgUvKdzZGZo+O6zNP72UjOv/bvV46qfpEQ1D98fx9rV4W4rpXbuMVFSZuV7q/23JDJELzrbyug0DVGRKqIiVURHCcfvHel4Bs6a4fkFt3RxCG/8PZHmFrvz95Y0NdtpabXT1Gx3eQbaDAP37jZQjbYYqJJSHu+WliWE2As0A/dJKb/xVFAI8RPgJ75e+KKrKln/VnKvSnnKRB1TJiq22A6Sk9Qc2jKa8LCBLRtUqx3D4L4Sn1Pslwe2g2+2eY9FKwRctDyMi5b3Tb5jhf13pxuoemxts5OQe9Kna1XX2HrtFIFD2V575dAsUHh1XQs3XB3pMS80RPCLW6P5xa3R7D1oprnZjloNsTEq4mPVJCd5HmEUFlu47IYqFs8P8Yty/+mPo3jovjh0A1xqm5ai4fJVfVO5Nht89GkbV99c7fP1BjqGuQb4V7fvFUCGlHImcBeOkHsebSP9DZB9OoXFFs5dU87u/UpM0v6gVokBKyRfEN4iRwyA8srAOZMKVD0OdFt9sLWZu+6rY8+B3uWZOVXHkoUhLJofwuQJOq+KHeCCKyqprbOxeYvRLyPGEL1qwIq9v6jVEB42sN/a59JCCA1wOfBWR5qU0iSlrHP+vxsoBMYNSMIeqKiyce6aCh5/xvMEm8LIZu0Pq6j1suJCwTPB1mbaDJLLrq/i6HH/BCPZtrO9M05xfaOdA4fP3EDgA3k1nA8clVKWdiQIIUYJ4YgkIIQYiyNA9omBidgzFovk3j/VE5FRzJSFpfzluSaKS3zb4dbaZmfjV0bu+K9aEsefZPX3K/0srYI/2XPARPrUEsLSi7j65uoeTTXesFik3xTLcKF7mwlJLRpQmwEG3Gaqqm3MWFJKSGoRy79X0a9gIs0tdl54rYXx804RklrE0ksqXPIfeLi+3/KMFITspSa7B8gGqoAHpJQvCCFeAr6TUj7X7djvAQ/iCI5tcx77Ua9CCOH3LsSk8TrmztJz5aXhjE7TEB+nJjREoNcJGpvsGIx2GprsnDxlpeiklXXvt7L3oHlAvZmc6+eQtnw8m697rTNNrddwzqvXsv+hjdTvL/PHrQUVS1691uV+A80NV0cyb7aeqZN0xMeqiIlWExkhaDdJWlolldVWKqpsbPjcQN4RC/sOmoI27NxQ09FmZk/Xc+6iEJc2YzJL6uptLm2msNjC7n0mdu71r5knI13DuQtDWThPz7TJeuJjHROV7SaJ0WinospG8SkreUfMPPLXRoztZ/Tvt9uraVtKGfAPDjd3w/6Tc/0cueTVa13S1CEauXTdD2XMpOSAyzcYn9PvN9g+ybOWu6WpNFoZkZLtlj7th/8tZ/7kCTnzJ0/IiOSsgMseiI8uRCUf/3JWv8qERqjls7vnypeOLZAvHVsgx82ODPh9nEGfXd70qrIodJCxtVv58sqXaDysmHiCB4G0uZshDrz0Ww698ccAyBNE+DBvaGy1cdvsHdy9dI//5VHwGUW5+xMZaAEU+oLdaqat2vNywt7MlCMds9Hus5KWXhyBKQSGM2vnjp9Jv3AiU+5eSt4TX6EJ1ZJ93Vm0V7luqVaHaFn56a3s+s1HVG8t7kyPm5HG3D9fwp77N2BpMhI/azTF7+zHanDM7msj9Mx5bA1lG/Op23UKXWwY0manIa+CUXMzmPmHC8l7/EuajlYz/6nvUfz2fgpf2wVA2orxNB2tpq20kdGrpzDxjkV8ufafmJvb0ceFkXXlTKq3FtFe18aS165j1z0fUbP9ZK/X7X6/jXkVJMxx3RDTcU9fX/86+thQt3saClJmryRh0kJUWh3NJUcwNbluTpp2w0Oo9SGc+OQFmkoO9+vcsTmzSJ55PvqoBMp3baDmwGaktBMzdjqx2TOJGj2B8h0fo9aFkDT9PJpKjtBYtJ/Y7JnYLWZixk6jfMfHpM65iKaSIxRvegUAffQoclfdhiYkAouhmdoj26jat8lvdQKQPSOS362bwg/HbetMGz0hnD9+OK0z7emdcwiL0iAELsd18OjnM4lP1VNbamL98+VsXlfV5+vPX5XAzQ9nU1du5qt1VXz6zwrsNslDH09nwwvlTJofzazlcVgtkv+3bA/Glq5VUMlZofxu3RT0YWqaqs1sfKWCT/5Z0Xne1belkTQmpPPcG54v97WaBoRKrSU8KoWWhpIej8vIXUbJcf/+vp5QlPsAGHPpVMo3HaN0vUNJJMzJIDwt2uUY6WWhrSZUCxJMdW005VfTcMjdbFO3p5ST7x5wfCnp8g2Sc8NcTr57gPLPjzmyPsxjzCVTO5V7zOQUyj7LB6D883ym3HUuEZlx1B8ox1Rv4OhzWzrP1XKiluhxiZ3Kvafrnn6/rSUNjLl0qts9GcqbMJQ3ebynwSZpxjJOffsObVVFRKaPJ3XuKsp3ru/MP/Dyvag0OiJTc/p13qj0CWQsvoKSb/6NoeYU49bciVCpqdr7OQDRGZOo2P0JqXMvpv7YLgrW/43cNXfSWLSf6IxJlHyzjvbGSlLnXkzBx8+Ru+ZOQvelYawrw25pp3jTa1gMzYQnZ5Gx5CqMdeU0nzrit3op3NdCeaHRJW3RZaM4lW/o/H7HnJ3oQ1X8bf88t/LLfpDM+/9XyokDrWRPj+C6B7LQaAWbXu/9N566OIYf/nEs967aT0pWKD96KBuNRvDRc44FBtc9MJZPXyznwSsOEp+iZ83t6bz1iON5jEnUce+bU/i/O/NpqDKTnhtGS33X6qYf/nEsL91/gqKDrZ3nDpRyt9ssvSr2oURR7gMgfHRsp4IFaC2ud1Pu3qjZfpJT6w9z9rNXkPfEV5R9dhS72XXNtjflGDk2ntgpKWRf6zpJLjQqpNVOa3E9oUmRGKtaSFo4FpvJSqtTSatDtYy9ciaJC7MITYpEGxVC9bbiPl339Pv1dk8LnrmC0vWHPd7TYNNQuJe6/O0AtDdWE5U+3v0g2f+dLcmzV1Bz6FsaChwmi9oj20iYeHancrfbrDQU7CF17ipqj2zDWFeGpbWxM6/+2C50ETGkzl1FW1UxltZGdJFxjuMMLVgMjhGfqbmWxKnnEJaQ5lflDvDNO9UIleP2VWrBgtUJ/Odvriu4vG36ueSOdH52tqPzUFlkZNToENbcnt4n5X7JHel8/lolVcXtVBW38+WbVSy9JrlTuRfntfLuX08BUF5g5JI70jvLLr06CaTk6HZH2L2qYtflrp+/Vsl3H9V25n35Zt9HE4FiqIxXinIfIN1ttHZL3xWZtEsOPfkVha/v4uxnryD7+7PZdsfbmBq6elJ2k/ukn1AJ1CFajr3wHaUbXBu/tDpaZsmHeVyw8TYsbWbaq1vYc996zI2OXtv03y4nIjOOg49+QfOxahY8vdbtGp6u6+l+vd1Tzg1zyL1xrsd7GmyMda7Kytjgn9FDaFwK4UmZJM1Y5pIuVI6dkjaTAbvV0aO0tjsCO3dM2tpMjvvvyO/IU6kdzU+l1ZO76nZ0kbEIoUKlC0GU+FexA2x5v4ZJC6I5tKWJKYtiCIvWsO3D3n3qRMZpiUpwdeJ14kArl/40nZhEHY3VPZvd0seHkTMrklW3pLmkqzWO2duig66BsEMju3afjpkU7pbfnVW3pLmd98NnSrGdtqQ5NDyBjPHLiY4fi1YXjrm9mZ2bHnbcX2wGMxbdye6vHsfQ4ng5hEelMGvJL9n79V9pbSojbewiUjMXog+Nod3YwK4vHnU5/4IL/oBGG8KhHS9RX+X6240Zv5yUMQtQa/TUVx3B2Fbr9X78iaLcB0BbaSNROV3+sCMy4/p9jvbqVjb/4DWWvHYtaSsncOLNniezpF3SUlhLRGYc7TXeH/odd71P3T73dfWj5o0h/7mtNBx0DF3DUvs20gD3+/VGwcs7KXprX5/vyZ+c/u6RNj+MHIRApdFRsWsDdfk7Xc9vtzmvK7snniZTz321MedeQ9m29zHUlmK3Wphw+d0Dl9kDzbUWFt2TyKEtTSy8bBT7vqinpaH3zUu6EPd1Fx3eJMztPY+ChAr0oWre/cspvn3X1U9KhwJub+vhHML7aEKo6PG8rmkmzMYm8ne/gdnUTFRcFrGJE2ioPkpLQwmG1mqS0mdTdMRhwksafRZtzRW0NpWRmnk2GeNWUJj3Pi2Np4iMySAlcwEVxV3zEts+eQC1Wkd0QrbbtdNzllJw8D1a6k8SM2ocWRMvpPjoJ97v2U8oyn0AbL11HeNums/5H95M7c5TVG8pIrKbgl/52W2o9Y4qPut/ViOtdjYsexqAxLOzGHfTfMJSo2ivbePEm3sofnt/n6777c1vkrggk7OfvYKonFG0FNVR+PouKjcXAqDSqpn318sBx8vAUNZE/t+3Uvl1Id/97F2m3L2UCbcvxNxgdOv99/V+1XotVd+6bj7uuKeIjFgMlc39uid/kTZvFTazkbbqk0Qkj2XUlMVU7NrgepBK7fh4QHS4KOyeLyUnPnuRrBU3AoLGogOExqcihIr647sGLHNITCKm5jq0EbGknHUB+phR4GeTTAeTF0Zz88M5jJ8TyV3nuL90O3rT3akrN/H6n4o5+5JRnDjQytjpEVz/QBYv/LYQQ7PVrayq2zmkHf73jqP89Knx7N5YjxCQPi4MlUaw9f3eRw2v/qGIP300nZxZkbTUWUhI1xMSpmb3xnqkHS79aToI2P1Z17m/+497z9hmM2GzmRk7eTX6sFiEEFQUb6Oh+igAB7c+x9zl91F0ZD1xiRNIzTyb7Rv/BED21EvJ++4fNNQ4/CMaW2tYvPpR6ioPYW5v7rpXL+a+4iMbqCpxdAoMrdXoQ/ruinwgKMp9AEi7JP8f28j/R9cbvPSTrkb56YpnvZat3lpE9dYir/mWVhO1u095L7+t2M1WDg7FfvazV/DV91/Bbrah0qpJOS+X6fetoHLFszQdrWLLj9/06bqe7rc/9zQUVB/4irT5a1BptDQV51G69b3OvJQ5F5E4dQkqjcPEIO022huqOPrOnwGYftOjnaaS3FW3I+029j3//wBoOnmIE5++SPKs5STNPJ/2+gq/rWg5+dWbTL7mPizGVqoPfInV0Nx7IR/Z+mEtK29IYf3zZdi7LV383i8zWHljSmcv/YXD8ykvMPC7NY6J9Y2vVHStlikz8e8/l/DN244e87X3Z7HkikS0ekfZe16dzNEdzTx87SEA9m5q4K+35fP7d6dis0oqiox8/Le+TXrWlZt46Jo87n1zClq9ioYqMxue7xqR/vW2fNbcnsbqW9M6z+1JuY+bcTVhkYkc3/82rU1l2G0WMies7Mw3m1o7FX3i6NnUVR3GYu4ewcr9pWe39dVthetIwi6HaB4q0LtTR9IO1WD4RI9PlBdt/qlLWlROglua8lE+Z9Jn4UUPydSshZ3fVSqNzJyw0uWY+OTJUqsLlwsvekjGJU3sTJ+/4n6ZkbvM5di55//W7RoqlcalXMdn3IwrXb5PPOt6f96b1x2qSs99hGGsaMZmsqKPC0NKScSYOMbfvICmfN/9QisoDHfaDfXEjhpHbcVB1Bo9Y8avoP20ic366qPkTvseVouRhur8zvSS41+QOWEF7cYGp819NCePbXS7hlCpEcJ9fmJU2gya6k7Q3HCS6LixxCT0bxmuryjKfYRhbm5n92//w5LXr0Ot09Be20rN9hKO/fO7QIumoBAwju1bR860y5mz7B4splZKC79Gp3cN5CHtNpLSZ1NauNnFfl5e9C1S2hgzbrlztUwju754pDM/c8JK0saeg0rdZe4ztFazZ/OTAJQVfk3WpItRq3XUVuZx4tAHQ3DHffAKOSRCDIJXSAUFBYUzAK9eIRXfMgoKCgojEEW5KygoKIxAgsXmXgu0Of8GOwkEv5zDQUZQ5PQ3ipz+ZTjIOcZbRlDY3AGEELt8DZY9lAwHOYeDjKDI6W8UOf3LcJHTG4pZRkFBQWEEoih3BQUFhRFIMCn3vwdagD4yHOQcDjKCIqe/UeT0L8NFTo8Ejc1dQUFBQcF/BFPPXUFBQUHBTwRcuQshLhBC5AshCoQQ9wRanu4IIYqFEAeFEPuEELucaXFCiI1CiOPOv7EBkOtFIUS1ECKvW5pHuYSD/3XW7wEhxKwAy/l7IUSZs073CSEu6pb3G6ec+UKIlZ7P6ncZRwshvhRCHBFCHBJC/NyZHlT12YOcwVafIUKIHUKI/U45/+BMzxJCbHfW51tCCJ0zXe/8XuDMzwywnC8JIYq61ecMZ3rA2pHPBNgbpBooBMYCOmA/MCnQXiq7yVcMJJyW9ihwj/P/e4BHAiDXOcAsIK83uYCLgA04fJbOB7YHWM7fA7/ycOwk5++vB7Kcz4V6CGRMAWY5/48EjjllCar67EHOYKtPAUQ4/9cC2531tA642pn+HHCb8//bgeec/18NvDVE9elNzpeAtR6OD1g78vUT6J77XKBASnlCSmkG3gQuCbBMvXEJ8LLz/5eBS4daACnl10D9acne5LoEeEU6+A6IEUKkBFBOb1wCvCmlNEkpi4ACHM/HoCKlrJBS7nH+3wIcAdIIsvrsQU5vBKo+pZSyI0SY1vmRwHnA28700+uzo57fBpYJIdydpw+dnN4IWDvylUAr9zSge2SIUnp+YIcaCXwmhNgthPiJMy1JSlkBjgYHJAZMOle8yRWMdXync2j7YjezVsDldJoEZuLoxQVtfZ4mJwRZfQoh1EKIfUA1sBHHqKFRStkRtqm7LJ1yOvObgPhAyCml7KjPh5z1+aQQQn+6nE6CoR31SKCVu6c3dDAt31kopZwFXAjcIYQ4J9AC+UCw1fGzQDYwA6gAHnemB1ROIUQE8A7wCyllT6GQgk3OoKtPKaVNSjkDSMcxWpjYgyxBI6cQYgrwG2ACMAeIA34daDl9JdDKvRQY3e17OtC3+FtDgJSy3Pm3GngPx4Na1TEcc/4NligY3uQKqjqWUlY5G5Ud+AddpoKAySmE0OJQmK9LKd91JgddfXqSMxjrswMpZSPwFQ4bdYwQosOXVXdZOuV05kfTd1Oev+W8wGn+klJKE/BPgqg++0uglftOINc5k67DMaHyYYBlAkAIES6EiOz4H1gB5OGQ7wbnYTcAQ+N5v3e8yfUhcL1ztn8+0NRhbggEp9kpL8NRp+CQ82rn6oksIBfYMQTyCOAF4IiU8oluWUFVn97kDML6HCWEiHH+Hwqcj2N+4EtgrfOw0+uzo57XAl9I5wxmAOQ82u2FLnDMC3Svz6BpR30i0DO6OGahj+Gwy90baHm6yTUWx2qD/cChDtlw2AM3Acedf+MCINu/cAzBLTh6FDd5kwvHcPJpZ/0eBM4KsJyvOuU4gKPBpHQ7/l6nnPnAhUMk4yIcw+sDwD7n56Jgq88e5Ay2+pwG7HXKkwfc70wfi+PlUgD8G9A700Oc3wuc+WMDLOcXzvrMA16ja0VNwNqRrx9lh6qCgoLCCCTQZhkFBQUFhUFAUe4KCgoKIxBFuSsoKCiMQBTlrqCgoDACUZS7goKCwghEUe4KCgoKIxBFuSsoKCiMQBTlrqCgoDAC+f81CaIav8YGdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# word cloud for negative words\n",
    "neg_words = ' '.join(neg_senti)\n",
    "wc_neg = wc.generate(neg_words)\n",
    "plt.imshow(wc_neg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A clear scenario of negative outcome has been put into explanation when crucial information is let free into the hands of data aiming markets and the consequences of it when the same information slides into the international markets that not only effects an individual but the entire nation.\n",
    "A syatem with no accountability, not only exposes preserved data but changes the intent that negatively impacts everyone.\n",
    "Thus the terms impose , critical , argue, fear , violation , burden , exploit , fraud are having negative score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion:\n",
    "In this way, we can extract words that are most repeated in the document by vectorization and also classify the positive, neagtive and neutral terms using Sentiment analyser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}