Review Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a96164f0",
   "metadata": {},
   "source": [
    "# How to Use NLTK to analyze a Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a286d0cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compound Score:  -0.7579\n",
      "Positive Score:  0.095\n",
      "Negative Score:  0.162\n",
      "Neutral Score:  0.743\n",
      "Sentiment Label:  Negative\n",
      "Subjective Label:  Subjective Comment\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\User\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "import re\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from textblob import TextBlob\n",
    "\n",
    "# Download the NLTK sentiment analysis package\n",
    "nltk.download('vader_lexicon')\n",
    "\n",
    "# Create an instance of the SentimentIntensityAnalyzer class\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Input text for sentiment analysis\n",
    "text = \"\"\"I cannot express enough how much I hated this movie. \n",
    "The story was so poorly written that I couldn't follow it, and the acting was even worse.\n",
    "It was painful to watch these actors stumble through their lines, and I found myself wishing for the movie to end.\n",
    "The special effects were also terrible, and I couldn't believe how cheap they looked.\n",
    "Save yourself the time and money and skip this movie altogether.\"\"\"\n",
    "\n",
    "# Analyze sentiment using the SentimentIntensityAnalyzer\n",
    "Polarity = analyzer.polarity_scores(text)\n",
    "\n",
    "blob = TextBlob(text)\n",
    "\n",
    "subjectivity = blob.sentiment.subjectivity\n",
    "\n",
    "# Extract the sentiment scores\n",
    "compound_score = Polarity['compound']\n",
    "positive_score = Polarity['pos']\n",
    "negative_score = Polarity['neg']\n",
    "neutral_score = Polarity['neu']\n",
    "\n",
    "# Print the sentiment scores\n",
    "print(\"Compound Score: \", compound_score)\n",
    "print(\"Positive Score: \", positive_score)\n",
    "print(\"Negative Score: \", negative_score)\n",
    "print(\"Neutral Score: \", neutral_score)\n",
    "\n",
    "# Determine the overall sentiment label\n",
    "if compound_score >= 0.05:\n",
    "    sentiment_label = \"Positive\"\n",
    "elif compound_score <= -0.05:\n",
    "    sentiment_label = \"Negative\"\n",
    "else:\n",
    "    sentiment_label = \"Neutral\"\n",
    "    \n",
    "if subjectivity > 0.5:\n",
    "    subjective_label = 'Subjective Comment'\n",
    "else:\n",
    "    subjective_label = 'Objective Comment'\n",
    "\n",
    "# Print the overall sentiment label\n",
    "print(\"Sentiment Label: \", sentiment_label)\n",
    "print(\"Subjective Label: \",subjective_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9861cb0a",
   "metadata": {},
   "source": [
    "# Web Scraping The Review Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90c5c7fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:00<00:00, 73.42it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 75/75 [00:00<00:00, 92.48it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 82.03it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 125/125 [00:01<00:00, 86.65it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 150/150 [00:01<00:00, 84.08it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 175/175 [00:02<00:00, 85.82it/s]\n",
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      "100%|████████████████████████████████████████████████████████████████████████████████| 250/250 [00:02<00:00, 89.60it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 275/275 [00:03<00:00, 84.23it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 300/300 [00:04<00:00, 63.25it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 325/325 [00:03<00:00, 83.23it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 350/350 [00:04<00:00, 76.01it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 375/375 [00:04<00:00, 78.53it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 425/425 [00:06<00:00, 61.46it/s]\n"
     ]
    }
   ],
   "source": [
    "from selenium import webdriver\n",
    "from selenium.webdriver.chrome.service import Service as ChromeService\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "from scrapy import Selector\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Specify the path to the Chrome driver executable\n",
    "chrome_driver_path = 'chromedriver.exe'\n",
    "\n",
    "# Create a Chrome service\n",
    "chrome_service = ChromeService(executable_path=chrome_driver_path)\n",
    "\n",
    "# Create a WebDriver instance\n",
    "driver = webdriver.Chrome(service=chrome_service)\n",
    "\n",
    "# Specify the URL to scrape\n",
    "url = 'https://www.imdb.com/title/tt10168312/reviews?ref_=tt_urv'\n",
    "\n",
    "# Load the URL in the WebDriver\n",
    "driver.get(url)\n",
    "\n",
    "# Wait for the reviews section to load\n",
    "reviews_section = WebDriverWait(driver, 10).until(\n",
    "    EC.presence_of_element_located((By.ID, 'content-2-wide'))\n",
    ")\n",
    "\n",
    "# Scroll down to the bottom of the page to load more reviews\n",
    "while True:\n",
    "    try:\n",
    "        driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n",
    "        time.sleep(1)\n",
    "        load_more_button = driver.find_element(By.CSS_SELECTOR, 'button.ipl-load-more__button')\n",
    "        if not load_more_button.is_displayed():\n",
    "            break\n",
    "        load_more_button.click()\n",
    "        # Wait for the new reviews to load\n",
    "        WebDriverWait(driver, 10).until(\n",
    "            EC.presence_of_element_located((By.CSS_SELECTOR, 'div.review-container'))\n",
    "        )\n",
    "    except:\n",
    "        break\n",
    "\n",
    "# Extract reviews from the loaded page\n",
    "reviews = driver.find_elements(By.CSS_SELECTOR, 'div.review-container')\n",
    "review_list = []\n",
    "\n",
    "# Loop through the reviews and extract the review text\n",
    "for d in tqdm(reviews):\n",
    "    try:\n",
    "        sel2 = Selector(text=d.get_attribute('innerHTML'))\n",
    "        try:\n",
    "            review = sel2.css('.text.show-more__control::text').extract_first()\n",
    "        except:\n",
    "            review = np.NaN\n",
    "        review_list.append(review)\n",
    "    except Exception as e:\n",
    "        error_msg_list.append(e)\n",
    "\n",
    "# Continue scrolling and loading more reviews until desired count is reached\n",
    "while len(reviews) < 500:\n",
    "    try:\n",
    "        driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n",
    "        time.sleep(1)\n",
    "        load_more_button = driver.find_element(By.CSS_SELECTOR, 'button.ipl-load-more__button')\n",
    "        if not load_more_button.is_displayed():\n",
    "            break\n",
    "        load_more_button.click()\n",
    "        # Wait for the new reviews to load\n",
    "        WebDriverWait(driver, 10).until(\n",
    "            EC.presence_of_element_located((By.CSS_SELECTOR, 'div.review-container'))\n",
    "        )\n",
    "    except:\n",
    "        break\n",
    "\n",
    "    # Extract reviews from the newly loaded page\n",
    "    reviews = driver.find_elements(By.CSS_SELECTOR, 'div.review-container')\n",
    "    for d in tqdm(reviews):\n",
    "        try:\n",
    "            sel2 = Selector(text=d.get_attribute('innerHTML'))\n",
    "            try:\n",
    "                review = sel2.css('.text.show-more__control::text').extract_first()\n",
    "            except:\n",
    "                review = np.NaN\n",
    "            review_list.append(review)\n",
    "        except Exception as e:\n",
    "            error_msg_list.append(e)\n",
    "\n",
    "# Create a DataFrame to store the extracted reviews\n",
    "review_df = pd.DataFrame({'Review': review_list})\n",
    "\n",
    "# Close the WebDriver\n",
    "driver.close()\n",
    "\n",
    "review_df.drop_duplicates(inplace=True)\n",
    "review_df.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cc573b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Now that the first season is over, I can confi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This is fun. I am enjoying every story.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A brilliant concept and a great way to view so...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Really enjoying this series &amp; the risks they'v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Six episodes in, I have yet to be entertained ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>My son (12 years old, has seen every MCU movie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Twightlight zone meets MCU, it's great if you ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Some of the premises in the show are cool and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>It's called what If for a reason,the stories w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>I am really enjoying this side of Marvel. But ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>424 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Review\n",
       "0    Now that the first season is over, I can confi...\n",
       "1              This is fun. I am enjoying every story.\n",
       "2    A brilliant concept and a great way to view so...\n",
       "3    Really enjoying this series & the risks they'v...\n",
       "4    Six episodes in, I have yet to be entertained ...\n",
       "..                                                 ...\n",
       "419  My son (12 years old, has seen every MCU movie...\n",
       "420  Twightlight zone meets MCU, it's great if you ...\n",
       "421  Some of the premises in the show are cool and ...\n",
       "422  It's called what If for a reason,the stories w...\n",
       "423  I am really enjoying this side of Marvel. But ...\n",
       "\n",
       "[424 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "72630150",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanReview(text):\n",
    "    text = re.sub('@[A-Za-z0-9_]+', '', text)\n",
    "    text = re.sub('#','',text)\n",
    "    text = re.sub('RT[\\s]+','',text)\n",
    "    text = re.sub('https?:\\/\\/\\S+', '', text) \n",
    "    text = re.sub('\\n',' ',text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e2273b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>Review</th>\n",
       "      <th>CleanedReview</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Now that the first season is over, I can confi...</td>\n",
       "      <td>Now that the first season is over, I can confi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This is fun. I am enjoying every story.</td>\n",
       "      <td>This is fun. I am enjoying every story.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A brilliant concept and a great way to view so...</td>\n",
       "      <td>A brilliant concept and a great way to view so...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Really enjoying this series &amp; the risks they'v...</td>\n",
       "      <td>Really enjoying this series &amp; the risks they'v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Six episodes in, I have yet to be entertained ...</td>\n",
       "      <td>Six episodes in, I have yet to be entertained ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  \\\n",
       "0  Now that the first season is over, I can confi...   \n",
       "1            This is fun. I am enjoying every story.   \n",
       "2  A brilliant concept and a great way to view so...   \n",
       "3  Really enjoying this series & the risks they'v...   \n",
       "4  Six episodes in, I have yet to be entertained ...   \n",
       "\n",
       "                                       CleanedReview  \n",
       "0  Now that the first season is over, I can confi...  \n",
       "1            This is fun. I am enjoying every story.  \n",
       "2  A brilliant concept and a great way to view so...  \n",
       "3  Really enjoying this series & the risks they'v...  \n",
       "4  Six episodes in, I have yet to be entertained ...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_df['CleanedReview'] = review_df['Review'].apply(cleanReview)\n",
    "review_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f3a697da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_sentiment(df, text_column):\n",
    "    \"\"\"\n",
    "    Analyzes sentiment of text in a DataFrame column using VADER Sentiment Analyzer.\n",
    "\n",
    "    Args:\n",
    "        df (pd.DataFrame): DataFrame containing reviews\n",
    "        text_column (str): Column name containing review text\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: DataFrame with added sentiment scores and labels\n",
    "    \"\"\"\n",
    "    # Create a sentiment analyzer instance\n",
    "    analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "    # Initialize lists to store sentiment scores and labels\n",
    "    compound_scores = []\n",
    "    positive_scores = []\n",
    "    negative_scores = []\n",
    "    neutral_scores = []\n",
    "    subjectivity_scores = []\n",
    "    sentiment_labels = []\n",
    "\n",
    "    # Loop through the rows in the DataFrame\n",
    "    for index, row in df.iterrows():\n",
    "        text = row[text_column]\n",
    "        scores = analyzer.polarity_scores(text)\n",
    "        compound_score = scores['compound']\n",
    "        positive_score = scores['pos']\n",
    "        negative_score = scores['neg']\n",
    "        neutral_score = scores['neu']\n",
    "        # assuming the DataFrame already has a 'subjectivity' column\n",
    "\n",
    "        # Determine the overall sentiment label\n",
    "        if compound_score >= 0.05:\n",
    "            sentiment_label = \"Positive\"\n",
    "        elif compound_score <= -0.05:\n",
    "            sentiment_label = \"Negative\"\n",
    "        else:\n",
    "            sentiment_label = \"Neutral\"\n",
    "\n",
    "        # Append sentiment scores, subjectivity score, and labels to the lists\n",
    "        compound_scores.append(compound_score)\n",
    "        positive_scores.append(positive_score)\n",
    "        negative_scores.append(negative_score)\n",
    "        neutral_scores.append(neutral_score)\n",
    "        sentiment_labels.append(sentiment_label)\n",
    "\n",
    "    # Add sentiment scores, subjectivity score, and labels as new columns to the DataFrame\n",
    "    df['Compound Score'] = compound_scores\n",
    "    df['Positive Score'] = positive_scores\n",
    "    df['Negative Score'] = negative_scores\n",
    "    df['Neutral Score'] = neutral_scores\n",
    "    df['Sentiment Label'] = sentiment_labels\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c6e979b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = analyze_sentiment(review_df, 'Review')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0cf6c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Positive    312\n",
       "Negative     76\n",
       "Neutral      36\n",
       "Name: Sentiment Label, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sentiment Label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47a27df5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "\n",
    "# Add a new column for the subjectivity score\n",
    "df['Subjectivity Score'] = df['Review'].apply(lambda x: TextBlob(x).sentiment.subjectivity)\n",
    "\n",
    "# Add a new column for the subjectivity label\n",
    "df['Subjectivity Label'] = df['Subjectivity Score'].apply(lambda x: 'Subjective' if x > 0.5 else 'Objective')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1576e49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "df['Word Count'] = df['CleanedReview'].apply(lambda x: len(word_tokenize(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "42eb34f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Review</th>\n",
       "      <th>CleanedReview</th>\n",
       "      <th>Compound Score</th>\n",
       "      <th>Positive Score</th>\n",
       "      <th>Negative Score</th>\n",
       "      <th>Neutral Score</th>\n",
       "      <th>Sentiment Label</th>\n",
       "      <th>Subjectivity Score</th>\n",
       "      <th>Subjectivity Label</th>\n",
       "      <th>Word Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Now that the first season is over, I can confi...</td>\n",
       "      <td>Now that the first season is over, I can confi...</td>\n",
       "      <td>0.5520</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.905</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.517708</td>\n",
       "      <td>Subjective</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This is fun. I am enjoying every story.</td>\n",
       "      <td>This is fun. I am enjoying every story.</td>\n",
       "      <td>0.7717</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.427</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Objective</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A brilliant concept and a great way to view so...</td>\n",
       "      <td>A brilliant concept and a great way to view so...</td>\n",
       "      <td>-0.8679</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.530</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0.651754</td>\n",
       "      <td>Subjective</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Really enjoying this series &amp; the risks they'v...</td>\n",
       "      <td>Really enjoying this series &amp; the risks they'v...</td>\n",
       "      <td>0.3804</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.580</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>Subjective</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Six episodes in, I have yet to be entertained ...</td>\n",
       "      <td>Six episodes in, I have yet to be entertained ...</td>\n",
       "      <td>-0.4147</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.698</td>\n",
       "      <td>Negative</td>\n",
       "      <td>0.863095</td>\n",
       "      <td>Subjective</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>My son (12 years old, has seen every MCU movie...</td>\n",
       "      <td>My son (12 years old, has seen every MCU movie...</td>\n",
       "      <td>0.9804</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.813</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.570333</td>\n",
       "      <td>Subjective</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Twightlight zone meets MCU, it's great if you ...</td>\n",
       "      <td>Twightlight zone meets MCU, it's great if you ...</td>\n",
       "      <td>0.8834</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.593</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>Subjective</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Some of the premises in the show are cool and ...</td>\n",
       "      <td>Some of the premises in the show are cool and ...</td>\n",
       "      <td>0.8248</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.664</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Subjective</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>It's called what If for a reason,the stories w...</td>\n",
       "      <td>It's called what If for a reason,the stories w...</td>\n",
       "      <td>0.4259</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.591</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>Subjective</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>I am really enjoying this side of Marvel. But ...</td>\n",
       "      <td>I am really enjoying this side of Marvel. But ...</td>\n",
       "      <td>0.6953</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.727</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>Subjective</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>424 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Review  \\\n",
       "0    Now that the first season is over, I can confi...   \n",
       "1              This is fun. I am enjoying every story.   \n",
       "2    A brilliant concept and a great way to view so...   \n",
       "3    Really enjoying this series & the risks they'v...   \n",
       "4    Six episodes in, I have yet to be entertained ...   \n",
       "..                                                 ...   \n",
       "419  My son (12 years old, has seen every MCU movie...   \n",
       "420  Twightlight zone meets MCU, it's great if you ...   \n",
       "421  Some of the premises in the show are cool and ...   \n",
       "422  It's called what If for a reason,the stories w...   \n",
       "423  I am really enjoying this side of Marvel. But ...   \n",
       "\n",
       "                                         CleanedReview  Compound Score  \\\n",
       "0    Now that the first season is over, I can confi...          0.5520   \n",
       "1              This is fun. I am enjoying every story.          0.7717   \n",
       "2    A brilliant concept and a great way to view so...         -0.8679   \n",
       "3    Really enjoying this series & the risks they'v...          0.3804   \n",
       "4    Six episodes in, I have yet to be entertained ...         -0.4147   \n",
       "..                                                 ...             ...   \n",
       "419  My son (12 years old, has seen every MCU movie...          0.9804   \n",
       "420  Twightlight zone meets MCU, it's great if you ...          0.8834   \n",
       "421  Some of the premises in the show are cool and ...          0.8248   \n",
       "422  It's called what If for a reason,the stories w...          0.4259   \n",
       "423  I am really enjoying this side of Marvel. But ...          0.6953   \n",
       "\n",
       "     Positive Score  Negative Score  Neutral Score Sentiment Label  \\\n",
       "0             0.058           0.037          0.905        Positive   \n",
       "1             0.573           0.000          0.427        Positive   \n",
       "2             0.214           0.256          0.530        Negative   \n",
       "3             0.268           0.152          0.580        Positive   \n",
       "4             0.133           0.170          0.698        Negative   \n",
       "..              ...             ...            ...             ...   \n",
       "419           0.158           0.030          0.813        Positive   \n",
       "420           0.407           0.000          0.593        Positive   \n",
       "421           0.237           0.100          0.664        Positive   \n",
       "422           0.244           0.165          0.591        Positive   \n",
       "423           0.273           0.000          0.727        Positive   \n",
       "\n",
       "     Subjectivity Score Subjectivity Label  Word Count  \n",
       "0              0.517708         Subjective         178  \n",
       "1              0.400000          Objective          10  \n",
       "2              0.651754         Subjective         107  \n",
       "3              0.800000         Subjective          13  \n",
       "4              0.863095         Subjective          75  \n",
       "..                  ...                ...         ...  \n",
       "419            0.570333         Subjective         252  \n",
       "420            0.700000         Subjective          24  \n",
       "421            0.541667         Subjective          52  \n",
       "422            0.750000         Subjective          78  \n",
       "423            0.600000         Subjective          30  \n",
       "\n",
       "[424 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb7455a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subjective    255\n",
       "Objective     169\n",
       "Name: Subjectivity Label, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Subjectivity Label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6120027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Positive    312\n",
       "Negative     76\n",
       "Neutral      36\n",
       "Name: Sentiment Label, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sentiment Label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f1efbae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ebb3735c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,5))\n",
    "color = ['green','red']\n",
    "df['Sentiment Label'].value_counts().plot(kind='bar',color = color)\n",
    "plt.title('Sentiment Among The Review')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Sentiment Label')\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c36b75c2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B16pgwYK2j3EvX76c5KM0Sfr0008lJQwFWbRoUbKgm1jZsmUzXF9mWT+qvHjxYqof365Zs8bW02Ud+mAPvXr1sv1/pRbS/Pz85O/vLyl7vbrS/3qQPTw81KlTpyT7nnrqKdsPvYz27o4aNSpJ0LXq2LGjypUrJylhnPs777yT4hj3QYMGqUCBApJkC7WJWXsKPTw89NlnnyUJLlYvvPCCWrVqJUnatGlTsl6wxLp27ZriklBOTk565ZVXJCV8hH3/eONr167ZPq3o2LFjkqBrVahQIdvrJC/x9vbWrFmzUvy3GzVqlG3ybEo3tLCO5XzyySeTBV0rZ2dnffLJJ8qfP7/CwsK0du3aHKw+YUz40aNHNWLECNt7SPXq1TV8+PBkbb/44gvFxMTIx8cnWdBN7N1331WpUqUUHx+f7LlufX+wWCypDuk5d+6cjh07Jinz7w+XL1/W6tWrJSWM774/6FpVrFhREydOlJTwfpR4WTpPT089+uijkpL32u7fv1+xsbHy8vLSiy++KCnhtZX4E8g7d+7YeoQZwuBYhF3kWSaTSXPmzNHatWvVsWPHZCst3Lp1S+vXr9ewYcPUrFmzTAXL7PDx8Ul1/U1JSSZZZWYm85kzZ2yTWHr27Jlm20ceecQWYhMHydu3b9sCRPfu3VPtLckNiUNdakMZrL02ZcuWVfPmze1WS6FChWw/LNetW2cbI5yYNQQXKlQoxZCVUWFhYdqwYYOklHurvby8bB/xrlixIkPDc6xjflPy2GOPSUp4vaTWrmDBgqpcubIkJZmUKEnXr1/XmTNnJCX06qbVW2j95TM+Pj7N8b9pjb1O/FHu/bX4+vraPrK+v0c8sVq1aqlmzZqp7neEHj162H6huJ+Hh4etV/P+xxwaGmoLUum97s1msy18pTX+NyPun9RZvnx5tW3bVitWrJCTk5O6d++u9evXp/h82Lx5s6SE95jUgq6UMF69YcOGKdZbs2ZN26cnqQ1lSPy+kdkhDFu3blVcXJzy5cunLl26pNm2WbNmkhJ+ATt69GiSfdb3pcRjb6X/hd/mzZurSZMmcnV1VUhISJIx7YnH+hJ2HYuwizyvdevWWr58uS5cuKA1a9bovffeU/fu3ZP0WJ47d07dunXTX3/9Zfd60gq6UsIPc+sPgD///DPD5038Jtu9e/dkqwvc/2WdxXzjxg3bcSdOnLB9/Ni0adMMX9seihcvrrZt20pSir3vly5d0v79+yUl9NrYe0iKdaJaZGSkfv755yT7wsPDbT1lPXr0SLHXPqPWrl1r6x1K7Qd04jGwGfmFKK3bYBcpUkSSbDO+02sXHh6eZPvp06dtf7cGk9Qk7l1OfNz90hqaUbRoUdvf768l8es3vVsq57Xxj+kNR7H+39z/mE+cOGELUdZVCtL6svZ0Jn7d57TSpUtr1KhR8vb2Trbv8uXLtsmFM2fOTLfedevWpVqv9XXw119/pTjx0RqCGzZsmOlVeazvpzExMSpRokSaNVrDbkp1WkNqaGhokhqtYbdFixYqVKiQ7edC4h5gxuvmHYRdPDDc3d3Vtm1bvf766/rhhx907tw5LV682DbRISwsLFfuV5/e3ZMKFCigwoULS1KGltWxSmt2eloSf+wWHBxs+3tKP6hym7WHLzg4ONmEolWrVtmCuT1WYbhfnTp1bAHq/qEMP//8s22c6P3Ln2WW9ePacuXKJfkhmljnzp1tgTojQxkKFSqU6j7rL1YFCxZM8xzWdvfPjL99+7bt74lvqpCSxM+pxMfdL61aEvcE3l9L4jvy3T+W937p1ZrbsvrvnxOv+6xIPKlzz549Wr16tV599VW5u7vrypUr6tOnj/z8/JIdl5P1Pv3007Z/l/t7dw8cOGBbTiwr7w85Vad13K70v9UXEo/XtYZh658phV3G6zoe//p4YLm4uKhHjx6qUqWK2rZtq3v37mn37t26fft2kt6jnGavHsjEPwQXL16cZm9eYnltndHEunbtartN7sqVK5OstmD94VazZk3bR7P2NmTIEB07dkyHDh3SmTNn9Mgjj0j638S0ypUrZ2s4xaVLl2wBISAgIEPPww0bNig0NDRbvck5xRETPh92iV/3M2fOVMuWLTN0XFq/AGXE/ZM6a9asqfbt26t3797q0qWLIiMjNWLECPn5+SV5biau97XXXsvwWNqUlvErU6aMmjdvLl9fX61evVoffPCBLfxahzC4uLikOYwnNdY6PTw8Ul21JiWlS5dO8r113O7p06e1Z88e23J6sbGxKl68uO29q2XLlvrkk09sQxfCwsIYr5uHEHbxwHvsscdUv3592+SACxcuJAkZ1jfP9MZGZrSnJL2PD6OjoxUWFiZJaU4Ou1/iniwPD48srQaQ+BxBQUGZPj6nFSpUSF27dtXy5cu1adMmhYeHy93dXcePH7eNE83sWLzsePrpp/Xuu+8qMjJSP/zwgz766CP9/ffftnHOzz77bLYC37Jly2y91RkVFRWlX375Jd27BNpL4teKdbmp1CR+TtnjF8rEv7gFBwen+QtAVnvu8prEr9mCBQs6/BbFjz/+uN588029//77unLlij777LMkq6kkrtfZ2Tnb9fbr10++vr4KDAzUnj171KpVK8XExGjNmjWSpPbt26fby58S6zHh4eGqUqVKhld6SUmLFi10+vRp7du3T7GxsUnG61o1atRIrq6utuEO165dY7xuHsIwBhiCdb1VKXnvlLu7u6SkH5GmJL2bH1iltPh4YseOHbO9yWXmB0Ht2rVtf09pxnxGz2F9/Cl9BJkROd27Zw2zkZGRtolb1l4bJycnPf3001k+d2Zr9fDwsC2ltGLFCsXExNiGNDg7O9tW0sgq6/rINWrU0MKFC9P9sg7BsR7nCIl71dNbzuvw4cO2v9sjlFl72iXZxqam5v6JRA+qmjVr2p7HWX3d57SRI0fanptffvllkuFRFSpUsP0SkhP19uzZ0zaxz/q+8Ntvv9mGgGV1iJP1rovx8fHZnsxnDathYWE6duyYLewm7oUvWLCgbWk0X19fxuvmMYRdPPAsFovtB6PJZEq2AoF1Tdbjx4+n2ut28+ZN7dq1K0PXO336dJo/iBOPB23Tpk2GziklBFXrUmA//PBDsoksGVG0aFHbDRQ2bNigy5cvZ/oc1h882blVbmKtW7dWyZIlJSUMXYiPj7dNEGvZsmWSX1QyKyu1Dh06VFLC//mmTZuS3PwhO7Xs27fPdhvnfv366amnnkr3q0ePHrZj75+ln1tKlSplC5nr169P81a+ixYtkpTwS4p1GbKc1KJFC9snMWn9AnDy5MkU71b1IPLy8lLjxo0lJUxuzMgtre0tf/78Gjt2rCQpIiJC8+bNs+1zdna2Lafn5+eX7i8l6Um8PN+6det09+5d2xCnwoULp7uSQmo6d+5s+yUicf1ZkXjc7pYtW5KN103cTkoYq8t43byFsIs8KTw8XO3atdOmTZuSTei437Rp02xLdjVt2jTZR17Wj5quX7+e4g9Q652loqOjM1zf2LFjUwyjO3bssK2x2rBhw3RnlCfm5OSk119/XVLCLP3hw4enObQiOjpa8+fPT1b3uHHjJCU8rqFDh6Y5kSjxTSWsrJOQrMEtu5ydnW1j7qx35Lp27Zqk7K+tm5VaGzVqZOvNnDhxoq5fvy4p5yamSekvIXV/O4vF4tDeXes6oSEhIXr99ddT/KXwu+++s60c0aVLF9v6vjmpTJkyevLJJyUlLB11/6oZUsKwD+tz3CgmTJggKeHTj8GDByfpSb1fXFycli9fnu5d7LJr6NChttfXggULknwy9tprr8nFxUUWi0XDhg1L8xc1i8WizZs3p/nLibX3NjQ0VCtXrrQtbda1a9d0J/6lpmrVqrb3nc2bN6d7m+WgoCAtXrw4xX3FihWzfZIxf/58xcbGqkSJEslW4LCG3b179zJeN4/h1w3kWUeOHNHAgQNVsmRJdenSRQ0bNlSFChVUuHBhhYeH688//9SKFStsH1Hlz59fH330UbLz9O/fXzNnztSdO3c0btw4XbhwQU888YScnZ116tQpffXVVzpz5owaNmyoP/74I9266tatq6NHj6p169Z69dVXVatWLUVERGjz5s2aP3++4uPj5erqqk8++STTj/m5557Tzp07tXbtWm3dulWNGjXSsGHD1LBhQ5nNZkVERMjf31/79u3Thg0bdOfOHQ0cODDJOTp27KjnnntO33//vY4ePapGjRrphRdeUNOmTWU2m223vtywYYOcnZ1tQwusGjduLF9fXx05ckSzZ89Whw4d5ObmJimhJ/X+CRwZ0a9fP82bN09xcXG2H+wFCxa09WxmVVZrHTx4sN5++23bGNTixYsnu/lDZljH3UoJH+9ndHJh48aNVapUKV27dk3Lli3TpEmTHDJJ7LnnntOqVau0b98+rVq1SlevXtWIESNUqVIlBQcHa9WqVbYwbjabNXPmTLvVMm3aNO3evVuRkZF68cUX5efnp+7du6tIkSI6ffq0Pv/8c/3111+2O/QZQfv27fXyyy/riy++0OHDh22v++bNm8vT01NRUVG6dOmSDh48qPXr1ysoKEh+fn62oQb2UKBAAb388st67733FBoaqq+++kpvvvmmpIShL9OmTdPEiRN14cIFtWjRQs8++6zatWsnb29v3bt3T4GBgfrjjz+0bt06Xb58WcuWLUt1beQnnnhCxYoV061bt/TOO+8oKipKUvbH88+aNUtHjx7V+fPnNW3aNP36668aNGiQfHx8VKBAAYWEhOivv/7S77//rh07duixxx5Ldex8ixYt9Oeff9rW6E4pxDZu3Fj58+dP0hFC2M0bCLvIk1xcXOTt7a2goCBdv35d3377rb799ttU25cpU0bz5s1LcWyUp6envvjiCw0bNkx3797Vf/7znyS/5bu4uGjmzJn6999/MxR2n3zySXXs2FEzZsywfdSXWIECBfTNN98kubVkRplMJi1cuFAlS5bUggULdOXKFU2dOjXV9m5ubnJ2dk62/dNPP1WhQoX05Zdf6ubNm5o+fXqKx6e08sDzzz+vhQsX6vbt2/rggw9stwW1tt+4cWOmH9fjjz+uGjVq6OzZs7aPyTt16pTtFQiyWuuAAQP0wQcf6O7du5ISfqjef9OSzNi4caPth2BGe3WlhP/vbt26acGCBbp48aL27duX6nJl9uTs7KylS5dq0KBB2rt3r/bt25fieMzSpUtr2bJldg1ZlStX1k8//aRnn31WERER+uabb/TNN98kaTNp0iTFx8fryJEjqd7I4UHz4YcfqlixYpoxY4aCg4P1ySefpPoLs6ura6487ueff15z5sxRcHCwvvrqK40ZM8a2rOKIESPk5uamiRMnKjw8XF999ZW++uqrFM/j5OSU5uoR+fLlU+/evbVw4ULb+0PJkiXVunXrbNVvNpu1detWDR8+XLt379ahQ4fSHJdufWwpadGihb7++usk39+vQIECql+/vm2+BON18w6GMSBPKlCggM6cOaNff/1V77zzjjp27KjKlSvbwl3hwoVVpUoV9ejRQ3PnztUff/yR5htj9+7d9dtvv6lXr14qUaKE8uXLp1KlSqlPnz7aunWr7WPcjHrzzTe1Zs0aderUSd7e3nJ1dVXZsmU1ePBg7d27N9Vb/WaENXz7+flp1KhRqlWrlsxms5ydneXh4aFHH31U/fr1s/VIp/Qxn5OTk6ZNmyZfX1+98MILqlGjhgoXLiwXFxd5eXmpRYsWevfdd5O8eVuVLl1aO3bs0ODBg1W5cuUc+6F6/0STnFhbN6u1FitWTE888YTt++zeHjgrQxisevXqleJ5cpvZbNaGDRu0cOFCdezYUd7e3sqXL5/MZrMaNWqkDz74QAcPHkwykdJe2rZtKz8/Pw0bNkzlypWTq6urSpQooY4dO2r16tV66623bCue5IUl23LK+PHjdfjwYb3++uuqX7++ihUrJhcXF7m7u6tq1arq2bOnZs+erb/++st2Rzx7cnNz0+jRoyUlDHGx3lbaatCgQTpx4oTeffddNWvWTF5eXnJxcVGhQoVUsWJFderUSdOmTdOJEyfSHeN9//vBU089lebd2TKqePHiWrdunVavXq0BAwaocuXKcnd3l4uLi4oVK6Z69erpxRdf1MqVK22fzqQk8bhd6/cpSTxpjfG6eYcpJCQkc+vkAMiyixcv2sbxfv3117m67BaSql+/vvz9/dWoUSP9+uuvji4HmdSzZ0/t2rVLTZo0ydQ6qgAePvTsArnI+lG3ZKweqQfN3r175e/vLyn7E9OQ+65du2b7qDjxLYwBICWEXSAXWWfoSsrwJCbkvP/+97+SEu4ilZ11fmEf1tVVUhIVFaXRo0crJiZGUu7elATAg4nBJICdRURE6O+//9aZM2dsk80qVKigatWqObiyh0dYWJhu3ryp0NBQLV++3DZs4aWXXsr2bVeR81577TXduXNHvXv31uOPP66iRYsqPDxcR48e1bfffmvrlR84cKDt5gEAkBrCLmBnR44cUffu3W3fm0wmTZkyxYEVPXzWrVunMWPGJNlWvXp1vfrqqw6qCOk5duxYmjcs6Ny5c5aW9wPw8CHsArnEw8NDjz/+uMaOHasOHTo4upyHkpOTk0qXLq0nnnhCb7/9Nr26edSUKVO0YcMG7d69W1evXlVwcLAsFou8vLzUsGFD9e/fP1vrIgN4uLAaAwAAAAyLCWoAAAAwLMIuAAAADIuwCwAAAMMi7AJ5WHR0tM6fP6/o6GhHlwIAmcL7F/IKwi6Qx8XFxTm6BADIEt6/kBcQdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhuXi6ALw8DHPMTu6BBhYyLgQR5cAAMhD6NkFAACAYeX5sBsYGKh58+apd+/eqlmzpooXL67q1atr8ODBOnToULL206dPl9lsTvXr0qVLKV5n+/bt6tKli8qWLaty5cqpW7du2rVrl70fHgAAAOwozw9jmD9/vubMmaNKlSqpbdu28vLykr+/vzZu3KiNGzfqm2++UZ8+fZId98wzz6h8+fLJthcpUiTZtuXLl2vkyJHy8vLSM888I0las2aNevXqpe+//149e/bM+QcGAAAAuzOFhIRYHF1EWtatW6dixYqpRYsWSbb7+fmpZ8+ecnNz09mzZ5U/f35JCT27M2fO1Pr169WyZct0zx8SEqI6derIxcVFu3fvVpkyZSRJV69eVatWrSRJx44dU+HChXP4kT28GLMLe2LMLpA3REdHKyAgQOXKlVOBAgUcXQ4eYnl+GEOPHj2SBV1JatasmVq2bKmQkBCdPn06y+f/5ZdfdOfOHY0YMcIWdCWpTJkyevHFFxUcHKwNGzZk+fwAAABwnDwfdtOSL18+SZKzs3OyfX5+fpozZ44+++wzbdiwQeHh4SmeY8+ePZKkdu3aJdvXvn17SdLevXtzqmQAAADkojw/Zjc1AQEB2rlzp0qWLKnHHnss2f7p06cn+b5IkSKaMWOGbUyulb+/vySpSpUqyc5h3WZtk57o6OgMtQNgP7wOgbzh3r17Sf4EclJmhsY8kGE3JiZGI0eO1N27dzV58uQkPbs1a9bUF198oRYtWqhkyZIKCgrS1q1bNW3aNI0ePVpFihRRly5dbO1DQ0MlSR4eHsmuYx2na22TnsDAQMXFxWXnoQHIpoCAAEeXACCRoKAgR5cAg3F2dlblypUz3P6BC7vx8fEaPXq0/Pz8NHToUA0YMCDJ/u7duyf5vkKFChoxYoRq1KihXr166cMPP0wSdnNS6dKl7XJeABlXrlw5R5cAQAk9ukFBQfL29parq6ujy8FD7IEKu/Hx8RozZoxWrlypfv36afbs2Rk+tnXr1qpUqZJOnz6t0NBQW0+u9c/Q0FAVK1YsyTFhYWFJ2qSH2aaA4/E6BPIWV1dXXpdwqAdmgpq1R3fp0qV6+umn9eWXX8rJKXPle3p6SpKioqJs29Ial5vWeF4AAADkfQ9E2LUG3WXLlqlPnz76+uuvU1yBIS0RERE6c+aM3NzcbKFXkpo3by5J2rFjR7Jjtm/fnqQNAAAAHix5Puxahy4sW7ZMvXr10vz581MNumFhYfrnn3+SbY+KitKrr76qsLAw9erVSy4u/xu90bt3b3l4eGj+/Pm6evWqbfvVq1e1YMECeXp6qlu3bjn/wAAAAGB3eX7M7syZM7V06VK5u7uratWq+vjjj5O16dq1q2rXrq1bt26pYcOGqlevnqpXry5vb2/duHFDu3bt0tWrV+Xj46OpU6cmOdZsNuvjjz/WyJEj1bp1a/Xu3VtSwu2Cb926pe+++467pwEAADyg8nzYvXz5siQpPDxcn3zySYptypcvr9q1a6to0aJ64YUXdPjwYW3btk0hISEqWLCgqlevrpEjR+rFF19UwYIFkx3fv39/eXp6atasWVqyZIlMJpPq1KmjCRMmqE2bNvZ8eAAAALAjU0hIiMXRReDhYp5jdnQJMLCQcSGOLgGAEm7wEhAQoHLlyrEaAxwqz4/ZBQAAALKKsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMCzCLgAAAAyLsAsAAADDIuwCAADAsAi7AAAAMKw8H3YDAwM1b9489e7dWzVr1lTx4sVVvXp1DR48WIcOHUrxmNDQUL399tuqWbOmSpQooVq1aum9995TeHh4iu3j4+P19ddfq1mzZipZsqSqVKmi4cOH6+LFi3Z8ZAAAALA3U0hIiMXRRaRl8uTJmjNnjipVqqQWLVrIy8tL/v7+2rhxoywWi7755hv16dPH1j4iIkKdOnXSyZMn1a5dO9WuXVsnTpzQjh07VK9ePW3atEkFChRIco2xY8dq8eLFevTRR/Xkk0/q2rVr+uWXX+Tm5qbffvtNVapUye2HbWjmOWZHlwADCxkX4ugSAEiKjo5WQECAypUrl+znLpCbXBxdQHrq1aunDRs2qEWLFkm2+/n5qWfPnho/fry6du2q/PnzS5L++9//6uTJkxo3bpwmT55sa28NzfPmzdP48eNt23fv3q3FixerWbNm+uWXX+Tq6ipJ6tu3r/r27asJEybo559/tv8DBQAAQI7L8z27aenTp4927Nih33//XXXr1pXFYpGPj4/CwsJ09uxZubm52dpGRESoRo0a8vLy0rFjx2zbX3jhBa1atUobN25U8+bNk5y/W7du2rNnj06ePKly5crl1sMyPHp2YU/07AJ5Az27yCvy/JjdtOTLl0+S5OzsLEny9/fXtWvX1Lhx4yRBV5Lc3NzUuHFjXbx4UVeuXLFt37Nnj9zc3NSkSZNk52/fvr0kae/evfZ6CAAAALCjPD+MITUBAQHauXOnSpYsqccee0xSQtiVpMqVK6d4TOXKlbV9+3b5+/urbNmyioiI0PXr1+Xj42MLzPe3T3ze9ERHR2floQDIQbwOgbzh3r17Sf4EclJmPi14IMNuTEyMRo4cqbt372ry5Mm2oBoaGipJKlKkSIrHeXh4JGln/dO6Pb326QkMDFRcXFwGHwUAewgICHB0CQASCQoKcnQJMBhnZ+dUOzZT8sCF3fj4eI0ePVp+fn4aOnSoBgwY4OiSbEqXLu3oEoCHHuPrgbzh3r17CgoKkre3t23yN+AID1TYjY+P15gxY7Ry5Ur169dPs2fPTrLf2hN7586dFI+/vyc3vZ7b9Hp+78cAfMDxeB0CeYurqyuvSzjUAzNBzdqju3TpUj399NP68ssv5eSUtHzrerjnz59P8RzW7dZ2bm5uKlmypC5dupTi8IP72wMAAODB8kCEXWvQXbZsmfr06aOvv/46xQllVapUUalSpXTgwAFFREQk2RcREaEDBw6oQoUKKlu2rG178+bNFRERof379yc73/bt2yVJzZo1y+FHBAAAgNyQ58OudejCsmXL1KtXL82fPz/FoCtJJpNJgwcPVnh4uD7++OMk+z7++GOFh4dr6NChSbZbv//oo4+SzBjdtm2b9uzZo3bt2ql8+fI5/KgAAACQG/L8TSWmT5+umTNnyt3dXS+99FKKQbdr166qXbu2pIQe3I4dO+rUqVNq166d6tSpo+PHj9tuF7xx40YVLFgwyfH33y74+vXrWrNmjdzc3LRt2zZVrVo1Vx7rw4KbSsCeuKkEkDdwUwnkFXl+gtrly5clSeHh4frkk09SbFO+fHlb2HVzc9PGjRs1Y8YMrV+/Xr6+vvL29tbLL7+sSZMmJQu6kjRnzhz5+Pho0aJF+uqrr+Tm5qZu3brpvffeU6VKlez34AAAAGBXeb5nF8ZDzy7siZ5dIG+gZxd5RZ4fswsAAABkFWEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhkXYBQAAgGERdgEAAGBYhF0AAAAYFmEXAAAAhuXi6AIAAHhQFDGbHV3CA6OIJG9HF/GAuRMS4ugSDImeXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGla2wW6dOHT3//PMZajt8+HA9/vjjWbrO8uXLNW7cOLVp00YlSpSQ2WzWTz/9lGLb6dOny2w2p/p16dKlFI/bvn27unTporJly6pcuXLq1q2bdu3alaV6AQAAkDe4ZOfgy5cvq3Tp0hlqGxQUpMuXL2fpOh9++KECAgLk6ekpb29vBQQEpHvMM888o/LlyyfbXqRIkWTbli9frpEjR8rLy0vPPPOMJGnNmjXq1auXvv/+e/Xs2TNLdQMAAMCxshV2MyM2NlZOTlnrSP78889VuXJllS9fXrNnz9YHH3yQ7jEDBw5Uy5Yt020XEhKiiRMnytPTU7t27VKZMmUkSePGjVOrVq00fvx4tWvXToULF85S7QAAAHCcXBmzGxMTI39/fxUtWjRLx7dp0ybFXtqc8Msvv+jOnTsaMWKELehKUpkyZfTiiy8qODhYGzZssMu1AQAAYF+Z6tndu3ev9uzZk2TblStXNHPmzFSPiYqK0r59+xQcHKwnnngia1VmgZ+fnw4fPiwnJydVrlxZbdq0kbu7e7J21sfTrl27ZPvat2+vGTNmaO/evbbhDQAAAHhwZCrs+vr6aubMmTKZTLZtV69eTTPsSpLFYlGhQoX0+uuvZ63KLJg+fXqS74sUKaIZM2YkC63+/v6SpCpVqiQ7h3WbtU16oqOjs1IqgBzE6xD2lHzWB5BzeP/KuAIFCmS4babCbq1atZKExaVLl6p48eJq3759iu1NJpMKFSqkSpUqqWfPnkmGCdhLzZo19cUXX6hFixYqWbKkgoKCtHXrVk2bNk2jR49WkSJF1KVLF1v70NBQSZKHh0eyc1nH6VrbpCcwMFBxcXE58CgAZFVGJrACWeXt6AJgaLx/ZYyzs7MqV66c4faZCrtdu3ZV165dbd8vXbpUlStX1rx58zJzGrvq3r17ku8rVKigESNGqEaNGurVq5c+/PDDJGE3J2V0ZQoA9lOuXDlHlwAAWcL7l31kazWG48ePZ6ob2ZFat26tSpUq6fTp0woNDbX15Fr/DA0NVbFixZIcExYWlqRNeh6UfwvAyHgdAnhQ8f5lH9lajaF8+fIqUaJETtVid56enpISJs1ZpTUuN63xvAAAAMj7cmyd3bCwMF24cEHh4eGyWCyptmvevHlOXTJTIiIidObMGbm5udlCr7WeVatWaceOHWrYsGGSY7Zv325rAwAAgAdPtsPusWPH9O6772rfvn1phlwpYcJacHBwdi+ZqrCwMAUFBalq1apJtkdFRenVV19VWFiYBg0aJBeX/z3s3r176//+7/80f/58Pfvss7ZJdFevXtWCBQvk6empbt262a1mAAAA2E+2wu6xY8fUtWtXRUVFyWKxKH/+/PLy8sryndJSs3jxYu3bt0+SdPr0aUnSDz/8YFsjt2nTphoyZIhu3bqlhg0bql69eqpevbq8vb1148YN7dq1S1evXpWPj4+mTp2a5Nxms1kff/yxRo4cqdatW6t3796SEm4XfOvWLX333XfcPQ0AAOABla2wO336dEVGRqpx48aaOXOm6tSpk1N1JbFv3z4tXbo0ybb9+/dr//79tu+HDBmiokWL6oUXXtDhw4e1bds2hYSEqGDBgqpevbpGjhypF198UQULFkx2/v79+8vT01OzZs3SkiVLZDKZVKdOHU2YMEFt2rSxy2MCAACA/ZlCQkLSHnuQhooVK+ru3bs6ffp0lm8FjIePeY7Z0SXAwELGhTi6BBhYEbPZ0SXAwO6EhDi6BEPK1niDe/fuqVq1agRdAAAA5EnZCruVKlVSZGRkTtUCAAAA5Khshd2BAwfq/PnzOnHiRE7VAwAAAOSYbIXdUaNGqW3bthoyZIgOHDiQUzUBAAAAOSJbqzG88sor8vLykq+vrzp37qzHHntMVatWVaFChVJsbzKZ9MUXX2TnkgAAAECGZWs1hqJFi8pkMqV7MwnbxUwm3bp1K6uXg0GwGgPsidUYYE+sxgB7YjUG+8hWz+6kSZNyqg4AAAAgx2Ur7L755ps5VQcAAACQ43L2vr4AAABAHkLYBQAAgGFlaxjD3r17M31M8+bNs3NJAAAAIMOyFXa7desmk8mU4fYmk0nBwcHZuSQAAACQYdkKu2XLlk017EZGRtqCraurq7y9vbNzKQAAACDTshV2T548meb+kJAQffPNN5o9e7aGDBmiN954IzuXAwAAADIlW2E3PWazWW+88YYqV66sF154QT4+PurSpYs9LwkAAADY5MpqDH369FGJEiU0d+7c3LgcAAAAICkXlx4rXbp0usMeAAAAgJyUK2E3Pj5e58+fV1xcXG5cDgAAAJCUC2E3JiZGb7/9tu7cuSMfHx97Xw4AAACwydYEtTFjxqS6z2Kx6ObNmzpx4oRu3rwpk8mUZnsAAAAgp2Ur7C5ZskQmk0kWiyXNdm5ubnr//ffVq1ev7FwOAAAAyJRshd1Jkyalus9kMqlQoUKqUqWKWrVqJXd39+xcCgAAAMi0bIXdN998M6fqAAAAAHJcri09BgAAAOS2HL2DWmxsrC5fvqywsDAVLlxY5cuXl4uLXW/SBgAAAKQqR5LokSNH9PHHH2vXrl2Kjo62bS9QoIDatm2rN954Q3Xr1s2JSwEAAAAZlu1hDIsWLVLHjh21detWRUVFyWKx2L6ioqK0adMmPfnkk1q8eHFO1AsAAABkWLbC7vHjx/X6668rNjZWTZo00ZIlS3Ts2DFdv35dx44d05IlS9S0aVPFxsZq/PjxOn78eE7VDQAAAKQrW2H3iy++UFxcnF5++WVt2rRJnTt3VoUKFZQ/f35VqFBBnTt31qZNm/TKK68oLi5Oc+fOzam6AQAAgHSZQkJC0r4jRBoee+wxRURE6O+//1a+fPlSbRcTE6OqVavK3d1df/75Z1YvB4MwzzE7ugQYWMi4EEeXAAMrYjY7ugQY2J2QEEeXYEjZ6tm9efOmqlSpkmbQlaR8+fKpatWq+vfff7NzOQAAACBTshV23d3dFRQUlKG2QUFBcnNzy87lAAAAgEzJVtitXbu2AgMDtWnTpjTbbdy4UVevXlXt2rWzczkAAAAgU7IVdp999llZLBaNGDFCX3zxhSIjI5Psj4yM1Oeff66RI0fKZDJp8ODB2SoWAAAAyIxsTVCTpKFDh2rdunUymUwqUKCAypcvrxIlSujGjRu6fPmyoqOjZbFY1LNnT33//fc5VDYeZExQgz0xQQ32xAQ12BMT1Owj2zeV+PbbbzVp0iS5u7srKipKZ8+ela+vr86ePauoqCi5u7vrzTff1MKFC3OiXgAAACDDst2zaxUZGal9+/bp77//Vnh4uNzd3VW9enU1adJEhQoVyolLwCDo2YU90bMLe6JnF/ZEz6595FjYBTKKsAt7IuzCngi7sCfCrn1kehhD165dVaxYMc2aNStD7WfNmqVixYqpd+/emS4OAAAAyI5MhV0/Pz/5+fnp8ccf1+uvv56hY15//XU9/vjj2rVrlw4ePJilIgEAAICsyFTYXb16tUwmk1577bVMXeT111+XxWLRypUrM3UcAAAAkB2ZCrsHDhxQgQIF9MQTT2TqIh06dFCBAgV04MCBTB0HAAAAZEemwu7ly5dVvnx5FShQIFMXyZ8/vypUqKBLly5l6jgAAAAgOzIVdq3r5maFdR1eAAAAILdkKuyazWYFBwdn6ULBwcEqUqRIlo4FAAAAsiJTYdc6FOHmzZuZusiNGzd06dIlVahQIVPHAQAAANmRqbDbsmVLScr0rX8XLlwoi8WiVq1aZeo4AAAAIDsyFXaHDh0qZ2dnzZkzR3v27MnQMb6+vpozZ45cXFw0ZMiQLBUJAAAAZEWmwm7FihX10ksv6e7du3rqqac0bdq0VMfwBgcH66OPPtLTTz+tmJgYjRgxQhUrVsyJmgEAAIAMMYWEhFgyc0B8fLyGDBmijRs3ymQyycnJSY888ogqVqwoNzc3RURE6OLFizpz5ozi4+NlsVjUpUsX/fDDD3JyyvTdiWFA5jlmR5cAAwsZF+LoEmBgRcxmR5cAA7sTEuLoEgwp02HX6vPPP9fs2bN1+/bt/53MZJLF8r/TFS1aVOPGjdPYsWOzXykMg7ALeyLswp4Iu7Anwq59ZDnsSlJkZKS2bdumffv2KTAwUGFhYSpcuLBKly6tpk2bqkOHDnJzc8vJemEAhF3YE2EX9kTYhT0Rdu0jW2EXyArCLuyJsAt7IuzCngi79sEgWgAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFgPRNhdvny5xo0bpzZt2qhEiRIym8366aefUm0fGhqqt99+WzVr1lSJEiVUq1YtvffeewoPD0+xfXx8vL7++ms1a9ZMJUuWVJUqVTR8+HBdvHjRTo8IAAAAueGBCLsffvihvv/+ewUEBMjb2zvNthEREeratavmzZun6tWra/To0apWrZo+//xz9ejRQ9HR0cmOGTdunCZNmiSLxaKRI0eqffv2Wr9+vdq2bSt/f397PSwAAADY2QMRdj///HOdOHFC/v7+ev7559Ns+9///lcnT57UuHHj9PPPP2vy5Mn6+eefNW7cOB05ckTz5s1L0n737t1avHixmjVrpl27dumDDz7Q/Pnz9dNPP+n27duaMGGCPR8aAAAA7OiBCLtt2rRR+fLl021nsVj0ww8/yN3dPVlInTBhgtzd3bV48eIk263fv/POO3J1dbVtf+KJJ9SiRQvt2LFDAQEBOfAoAAAAkNtcHF1ATvL399e1a9fUvn17ubm5Jdnn5uamxo0ba/v27bpy5YrKli0rSdqzZ4/c3NzUpEmTZOdr37699uzZo71792rAgAHpXj+lIRIAchevQ9hTEUcXAEPj/SvjChQokOG2hgu7klS5cuUU91euXFnbt2+Xv7+/ypYtq4iICF2/fl0+Pj5ydnZOsX3i86YnMDBQcXFxWaweQE7gkxjYU9qzRoDs4f0rY5ydnVPNeikxVNgNDQ2VJBUpkvLv3h4eHknaWf+0bk+vfXpKly6d8WIB2EW5cuUcXQIAZAnvX/ZhqLDraJnpUgdgH7wOATyoeP+yjwdiglpGWXti79y5k+L++3ty0+u5Ta/nFwAAAHmbocJulSpVJEnnz59Pcb91u7Wdm5ubSpYsqUuXLqU41vb+9gAAAHiwGC7slipVSgcOHFBERESSfRERETpw4IAqVKhgW4lBkpo3b66IiAjt378/2fm2b98uSWrWrJl9CwcAAIBdGCrsmkwmDR48WOHh4fr444+T7Pv4448VHh6uoUOHJtlu/f6jjz7SvXv3bNu3bdumPXv2qF27dhla4xcAAAB5jykkJMTi6CLSs3jxYu3bt0+SdPr0aR0/flxNmjRRpUqVJElNmzbVkCFDJCX04Hbs2FGnTp1Su3btVKdOHR0/flw7duxQvXr1tHHjRhUsWDDJ+ceOHavFixfr0Ucf1ZNPPqnr169rzZo1cnNz07Zt21S1atXcfcAGZ55jdnQJMLCQcSGOLgEGVsRsdnQJMLA7ISGOLsGQHoiwO2rUKC1dujTV/c8884y+/PJL2/d37tzRjBkztH79egUFBcnb21u9evXSpEmTVLhw4WTHx8fHa/78+Vq0aJHOnz8vNzc3tWnTRu+9954tUCPnEHZhT4Rd2BNhF/ZE2LWPByLswlgIu7Anwi7sibALeyLs2oehxuwCAAAAiRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGRdgFAACAYRF2AQAAYFiEXQAAABgWYRcAAACGZciwW6tWLZnN5hS/unbtmqz93bt3NXPmTNWrV0/e3t565JFH9Oqrr+rmzZsOqB4AAAA5xcXRBdiLh4eHRo0alWx7+fLlk3wfHx+vgQMHavv27WrYsKF69Oghf39/LV68WLt27dJvv/0mLy+v3CobAAAAOciwYbdIkSJ666230m23ZMkSbd++XU8//bQWLFggk8kkSfr22281fvx4ffjhh5ozZ46dqwUAAIA9GHIYQ2YsXrxYkvT+++/bgq4kDRs2TBUrVtTKlSsVFRXlqPIAAACQDYYNu/fu3dNPP/2kWbNmaf78+Tp06FCyNtHR0Tp06JCqVauWbHiDyWRS27ZtFRERoaNHj+ZW2QAAAMhBhh3GEBQUpDFjxiTZVq9ePS1cuFCVKlWSJF24cEHx8fGqXLlyiuewbvf391ezZs3SvWZ0dHQ2qwaQXbwOYU9FHF0ADI33r4wrUKBAhtsaMuwOGjRITZs2lY+Pj9zc3PTPP/9o7ty5Wr58uXr06CE/Pz8VLlxYoaGhkhLG96bEw8NDkmzt0hMYGKi4uLiceRAAsiQgIMDRJcDAvB1dAAyN96+McXZ2TrWjMiWGDLtvvvlmku9r166tr7/+WpK0fPlyLVq0SC+//HKOX7d06dI5fk4AmVOuXDlHlwAAWcL7l30YMuymZtiwYVq+fLkOHDigl19+2dZze+fOnRTbW3t0re3Sk5kudQD2wesQwIOK9y/7MOwEtZR4enpKkiIjIyVJFStWlJOTk86fP59ie+v2KlWq5E6BAAAAyFEPVdi1rshgXXmhYMGCql+/vv7++29dvnw5SVuLxaLff/9dbm5uqlu3bq7XCgAAgOwzXNg9d+6cref2/u2TJ0+WJD399NO27UOHDpUkTZkyRRaLxbb9u+++08WLF9W3b18VLFjQvkUDAADALgw3Znf16tWaN2+emjVrpnLlyqlQoUL6559/tG3bNsXExGj8+PFq3ry5rf3AgQO1Zs0arVq1SpcuXVLz5s11/vx5rV+/XhUqVNC7777rwEcDAACA7DCFhIRY0m/24NizZ48WLlyoEydO6ObNm4qMjJSnp6fq16+vF154Qe3atUt2zN27dzV79mwtX75cV69eVdGiRdWxY0e9++67KlGihAMehbGZ55gdXQIMLGRciKNLgIEVMZsdXQIM7E5IiKNLMCTDhV3kfYRd2BNhF/ZE2IU9EXbtw3BjdgEAAAArwi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswi4AAAAMi7ALAAAAwyLsAgAAwLAIuwAAADAswm4iR44cUd++fVW+fHmVLl1aHTp00Jo1axxdFgAAALLIxdEF5BW7d+/WU089pQIFCqhPnz5yd3fXunXrNGzYMF25ckWvvPKKo0sEAABAJplCQkIsji7C0WJjY9WwYUMFBgZq27Ztql27tiTpzp07at++vS5fvqxDhw6pfPnyDq7UGMxzzI4uAQYWMi7E0SXAwIqYzY4uAQZ2JyTE0SUYEj27SujVvXDhggYNGmQLupJUpEgRjR8/XqNHj9bSpUs1adIkB1ZpHJ4FPR1dAgBkSbwn71/Ag4awK2nPnj2SpHbt2iXb1759e0nS3r17c7UmI/Mf6e/oEgAgS8L8ef8CHjRMUJPk///fvKpUqZJsn7e3t9zd3XX+/PncLgsAAADZRNiVFBoaKkny8PBIcX/hwoVtbQAAAPDgIOwCAADAsAi7+l+Pbmq9t2FhYan2+gIAACDvIuzqf2N1/VOYeBAUFKTw8HBVrlw5t8sCAABANhF2JTVv3lyStGPHjmT7tm/fnqQNAAAAHhzcVEIJN5Vo0KCBrl27lupNJf744w9VqFDBwZUCAAAgMwi7/19qtwsOCAjQ1KlTuV0wAADAA4iwm8jhw4c1ffp0HTx4UDExMfLx8dGYMWPUp08fR5cGAACALCDsAgAAwLC4XTCQB61fv16rV6/WuXPnFBUVpaNHj0qSzp07p82bN6tv374qXbq0g6sEgOSCgoK0fv162/vX559/Lkn6999/denSJfn4+KhgwYIOrhIPE3p2gTwkPj5ew4cP19q1ayVJBQsWVFRUlG7duiVJunHjhnx8fPT2229r/PjxjiwVAJL55ptv9O677+ru3buSJJPJZHv/+uuvv9S8eXPNnj1bQ4cOdWSZeMiw9BiQh8ydO1e//PKLhg0bposXL+rll19Osr9EiRJq2rSpfv31VwdVCAAp27x5syZMmCAfHx8tXbpUw4cPT7L/0Ucf1WOPPaaNGzc6qEI8rAi7QB6ydOlS1atXT7NmzZKHh4dMJlOyNpUrV9alS5ccUB0ApO6zzz5T2bJltX79enXq1EleXl7J2vj4+OjMmTMOqA4PM8IukIecP39eTZs2TbNNsWLFbB8LAkBecerUKXXs2FFubm6ptildurRu3ryZi1UBhF0gTylQoIBCQ0PTbBMQEKAiRYrkUkUAkDHx8fFycUl73vvNmzfl6uqaSxUBCQi7QB5Su3Zt7dixQ9HR0Snuv337tn777Tc1aNAglysDgLRVrVpV+/btS3V/bGys/Pz85OPjk4tVAYRdIE8ZOXKkrl69qiFDhujq1atJ9l24cEGDBg1SaGioRo4c6aAKASBlffv21YkTJzRjxoxk++Li4vTuu+/q4sWLeuaZZxxQHR5mLD0G5DEffPCB5syZI5PJJDc3N0VERNjG6VosFk2YMEFvv/22o8sEgCRiYmLUu3dv+fn5qVKlSsqfP7/OnDmjHj166OjRo7p8+bLatWunVatWpTj5FrAXwi6QB/3++++aP3++Dh8+rNu3b6tw4cJq0KCBRo4cqfbt2zu6PABI0b179zRz5kx9++23CgkJsW0vXLiwhg8frrfeeosxu8h1hF0AAJCjLBaL/v77b9sv6zVq1JCzs7Ojy8JDirAL5CHnz59X5cqVHV0GAACGwQQ1IA+pX7++OnbsqG+//Va3b992dDkAkGE1a9bU1KlTdfbsWUeXAiRBzy6QhwwYMEA7duxQbGys8uXLpw4dOqh///7q1KkT49wA5GmVK1fW7du3ZTKZVLt2bfXv319PP/20ihcv7ujS8JAj7AJ5zK1bt7Rq1SotX75cR44ckclkkoeHh3r16qV+/fqpWbNmji4RAJKJjY3V1q1btXz5cv3666+6e/euXFxc1LZtW/Xr109du3ZVwYIFHV0mHkKEXSAPO3/+vJYuXaqVK1fq0qVLMplMKleunPr27at3333X0eUBQIru3LmjX375RcuWLdOBAwckSW5uburevbv69eunNm3aOLZAPFQIu8AD4sCBA1q2bJl++uknxcbG6tatW44uCQDSdenSJa1cuVIrVqzQ33//LScnJwUHBzu6LDxEmKAGPAD8/f21fft27dy5UzExMY4uBwAyrEKFCmrUqJEaNGigfPnyyWKhjw25y8XRBQBIWXBwsFatWqUVK1bo6NGjslgsMpvNGjZsmPr37+/o8gAgTX/++adWrFih1atXKzAwUBaLRdWqVeP9C7mOYQxAHhIdHa2NGzdqxYoV+v333xUTEyNXV1c98cQT6t+/vzp27MiqDADyrGvXrtkm2J4+fVoWi0VeXl7q06ePBgwYoLp16zq6RDyE6NkF8pDq1asrPDxcFotFjRo1Uv/+/dWnTx+ZzWZHlwYAaerZs6f27t2ruLg4FShQQL169VL//v3VoUMH7p4Gh6JnF8hD6tatq/79+6t///6qVKmSo8sBgAwrVqyYmjVrpv79+6tnz57y8PBwdEmAJMIuAADIAQEBASpXrpyjywCSIewCAADAsBizCzjQzJkzZTKZ9OKLL6po0aKaOXNmho4zmUyaOHGinasDgNTt3btXklS/fn0VKFDA9n1GNG/e3F5lAcnQsws4UNGiRWUymXTw4EFVrVpVRYsWzdBxJpOJm0oAcKiU3r9MJlOGjuX9C7mJnl3AgdavXy9JKlu2bJLvASCvmzhxokwmkzw9PZN8D+Q19OwCAADAsLhdMJCHLF26VKdOnUqzzenTp7V06dJcqggAMiYgIEChoaFptgkLC1NAQEAuVQQkIOwCecjo0aO1cePGNNts2rRJY8aMyaWKACBj6tSpo6+++irNNl9//bXq1KmTSxUBCQi7wAMmLi5OTk68dAHkLRaLRRZL2iMj09sP2AM/MYEHzIkTJzK8agMA5CWBgYFyd3d3dBl4yLAaA+Bg3bt3T/L9kiVLtGfPnmTt4uLiFBgYqMuXL6t37965VR4ApOr+tcFTeu+SEt6/rl69qp9//lkNGjTIjdIAG1ZjABwscS+tyWRK9WM+JycnFS1aVK1atdKMGTNUokSJ3CoRAFKU0fcvq1KlSunHH39UvXr17F0aYEPYBfKQokWL6s0339SkSZMcXQoApMvak2uxWNSjRw8NHDhQzzzzTLJ2zs7OKlq0qKpXr86cA+Q6wi6Qh+zZs0fly5dX+fLlHV0KAGTKjBkz1KJFC7Vo0cLRpQBJEHaBPCQuLk4RERFyd3dPsffDut/NzU3Ozs4OqBAAgAcLnyUAeciMGTNUrVq1VO8bf/v2bVWvXl2ffPJJLlcGAGlbsmSJWrdurWvXrqW4/9q1a2rdurVWrlyZy5XhYUfYBfKQrVu3qnXr1vLy8kpxv5eXl1q3bq1NmzblcmUAkLYlS5bI1dVVpUqVSnF/qVKlVLBgQf3www+5XBkedoRdIA+5ePGiqlWrlmabatWq6dKlS7lUEQBkzNmzZ1W7du0029SqVUtnz57NpYqABIRdIA+JjY1Nd6ayyWTS3bt3c6kiAMiY0NBQmc3mNNsULlxYISEhuVIPYEXYBfKQSpUqaffu3Wm22b17typUqJBLFQFAxpQsWVInT55Ms82pU6dUvHjxXKoISEDYBfKQ7t276+TJk/roo48UFxeXZF9cXJw+/PBDnTx5Uj179nRQhQCQsjZt2mj79u36/fffU9y/Y8cO/fbbb2rfvn0uV4aHHUuPAXlIeHi42rVrp3/++UeVKlVSy5YtVapUKV27dk2+vr66cOGCatSooW3btnF/eQB5ysWLF9WqVStFRkaqf//+atu2re39a8eOHVqxYoXc3Ny0a9cuVaxY0dHl4iFC2AXymH///Vfjx4/Xhg0bktx608nJST169NCsWbNUrFgxB1YIACnbt2+fhg8frmvXrslkMtm2WywWlSlTRt99950aNmzowArxMCLsAnnUjRs3dPToUYWGhqpIkSKqW7cuY90A5Hn37t3Tpk2bdPjwYdv7V/369dW5c2e5uro6ujw8hAi7AAAAMCwXRxcAILl79+5p586dOnfunCIjIzVx4kRJUnR0tMLCwuTp6ZnuEmUA4Cjh4eH6559/FBkZqWbNmjm6HDzk+GkJ5DGbNm1SzZo1NWDAAL333nuaMWOGbd+ff/6pGjVqaPXq1Q6sEABSdunSJT3zzDOqWLGi2rVrp+7du9v27d+/X40bN5avr68DK8TDiLAL5CH79+/X0KFD5erqqhkzZqhv375J9tevX1+VK1fWunXrHFQhAKQsICBATzzxhLZt26YuXbqoUaNGSSbZNmjQQMHBwfyyjlzHMAYgD/n4449VpEgR7dq1S56enrp161ayNnXr1tWhQ4ccUB0ApG769OkKCQnRxo0b1bhxY82YMUMHDx607XdxcVHTpk114MABB1aJhxE9u0AecujQIXXp0kWenp6ptilTpoxu3LiRi1UBQPp27Nihbt26qXHjxqm2KVeunAIDA3OxKoCwC+Qp9+7dU+HChdNsc+fOHSanAchzbt++rfLly6fZxmKx6N69e7lUEZCAn5hAHlKxYkUdOXIkzTYHDx5UtWrVcqkiAMiY4sWLy9/fP802p0+fVtmyZXOpIiABYRfIQ7p3764DBw7oxx9/THH/559/rr/++ku9e/fO5coAIG1t27bV1q1bderUqRT3+/n5affu3XriiSdyuTI87LipBJCHhIeH64knntDZs2fVqlUr3b17VwcOHNDLL7+sP/74QwcOHFCtWrX066+/Kn/+/I4uFwBsLl26pJYtW0qSxo4dq3PnzmnVqlVavny5Dhw4oHnz5qlQoULas2ePSpYs6eBq8TAh7AJ5TEhIiCZMmKA1a9YoLi7Ott1kMql3796aNWuWzGaz4woEgFQcOnRIzz//vAICAmQymWSxWGx/li1bVosXL1bdunUdXSYeMoRdII+6deuWjhw5otu3b6tw4cKqV6+eSpQo4eiyACBNsbGx2rx5sw4fPmx7/2rQoIG6dOkiV1dXR5eHhxBhFwAAAIbFBDUAAAAYFndQAxxozJgxMplMev/991WiRAmNGTMmw8fmz59fpUuXVpcuXeTj42PHKgEguaVLl0qSunXrpsKFC9u+zwjr+1fDhg3l7OxsrxIBSQxjAByqaNGiMplMOnjwoKpWraqiRYtm+hzOzs764Ycf1LlzZztUCAApS+n9y2QyZeocpUuX1urVq1WjRg07VQkQdgGHunz5sqSEN3wXFxfb9xlx9+5d+fv764033lCxYsW0e/due5UJAMn89NNPMplM6t69uwoXLqwlS5Zk+Fjr+9e3336rxo0ba82aNXasFA87wi7wgJs8ebLmz5/P/eYBPHDeeOMNrVq1ShcvXnR0KTAwxuwCD7jBgwerYcOGji4DADKtW7duypcvn6PLgMHRswvkQevXr9eSJUt08uRJhYaGysPDQ7Vr19bAgQPVrVs3R5cHAKm6fv26Vq9erRMnTiR5/3rqqae4cxocgrAL5CGxsbF64YUXtG7dOlksFrm4uKhYsWK6deuWYmNjZTKZ1KNHD33zzTdyceGDGQB5y4IFC/T+++/r7t27sliSxosCBQpo6tSpeuGFFxxUHR5WrLML5CGffvqp1q5dq6ZNm2rz5s0KCgrS2bNnFRQUpE2bNqlJkyZat26dZs+e7ehSASCJ1atXa+LEiXJzc9Pbb7+tDRs26ODBg9qwYYPefvttFSpUSBMnTmQyGnIdPbtAHlKnTh0VKFBAe/fuTbHnNiYmRs2bN9fdu3d1/PhxB1QIAClr1aqVAgMD5evrq1KlSiXbf/XqVbVq1Uply5bVrl27HFAhHlb07AJ5SFBQkDp16pTqEIV8+fKpU6dOCgoKyuXKACBt586dU+/evVMMupJUpkwZ9erVS+fOncvlyvCwI+wCeUiZMmUUERGRZpvIyEiVLVs2lyoCgIwpUqSIChUqlGYbNzc3FSlSJJcqAhIQdoE8ZMiQIVqzZo2uX7+e4v7AwED9/PPPGjJkSC5XBgBp69y5s7Zs2aLY2NgU98fExGjLli3q0qVLLleGhx1jdgEHCggISPJ9fHy83nzzTR0+fFijRo1SkyZNVKJECd24cUP79u3TV199pQYNGmj69OmqUKGCg6oGgOTu3LmjXr16yd3dXe+//36S9b8PHjyoKVOmKDIyUmvWrKF3F7mKsAs4UGr3krdYLKlulyQnJycFBwfbvT4ASE2dOnWSbYuJibF9MuXi4iJPT08FBwfbentLliwpV1dXHTt2LDdLxUOOhToBBxowYECKoRYA8rr4+Phk718uLi7J5hTcfyOJ+Ph4u9cGJEbPLgAAAAyLnl0AAJBjAgMDbcsjent7q3Tp0g6uCA87wi4AAMiW8PBwff755/rpp58UGBiYZF/p0qX17LPP6uWXX5a7u7uDKsTDjGEMQB6S0oSPlJhMJiZ4AMgTLly4oKeffloXLlyQxWJRqVKlVKZMGUkJd027du2aTCaTKleurFWrVqlixYqOLRgPHXp2gTwkpQkfkhQaGqo7d+5ISpjskS9fvtwuDQCSuXv3rvr166fz58+rb9++mjBhgqpVq5akzd9//62PP/5YK1euVL9+/eTr66v8+fM7qGI8jAi7QB5y8uTJVPddunRJ77zzjm7evKk1a9bkYlUAkLKFCxfqn3/+0aRJk/Tmm2+m2KZatWqaP3++qlatqunTp+vbb7/VqFGjcrlSPMwYxgA8QGJiYtSiRQu1a9dO06dPd3Q5AB5ynTt31o0bN3To0KF0l1G0WCxq2LChvLy8tGXLllyqEOB2wcADJV++fGrTpo1++eUXR5cCADpz5ozatWuXofXCTSaT2rZtq7Nnz+ZCZcD/EHaBB0xUVJRu377t6DIAQJGRkfLw8Mhwew8PD0VGRtqxIiA5wi7wAPHz89Pq1atVtWpVR5cCACpevLjOnz+f4fYXLlyQl5eXHSsCkmOCGpCHdO/ePcXtsbGxunbtmi5fvixJmjhxYm6WBQApatiwoX777TcFBQXJ29s7zbZBQUH69ddf9eSTT+ZSdUACJqgBeUjRokVT3G4ymWQ2m1WvXj2NGTNGbdu2zeXKACC53bt3q2fPnmrQoIGWLVsmT0/PFNvdunVL/fv31+HDh7VmzRq1bt06lyvFw4ywCwAAsmzcuHFatGiRihUrpmHDhqlly5YqW7asJOnKlSvavXu3Fi1apODgYA0ePFifffaZgyvGw4awC+RxsbGx+vPPPyVJPj4+3FACQJ4SFxenyZMna968ebJYkkcKi8UiJycnvfTSS5oyZYqcnZ0dUCUeZoRdwMEuXrwoX19fNW3aNNnEs82bN2vs2LEKDg6WJJnNZs2aNUu9e/d2RKkAkCp/f38tWbJEBw8e1I0bNyRJJUqUUOPGjTVgwAAm1sJhCLuAg02ZMkVz5szRsWPHVL58edv28+fPq3nz5oqOjla5cuXk5uams2fPysnJSdu3b1edOnUcWDUAAA8Glh4DHGzfvn2qVatWkqArSV9++aWio6P1wgsv6MSJE9q3b58WL16suLg4LViwwEHVAgDwYCHsAg52+fJl1a9fP9n27du3y9XVVe+//75tW7du3dS0aVPt27cvN0sEAOCBRdgFHOzff/9VsWLFkmy7ffu2Lly4oPr166tw4cJJ9tWuXVvXrl3LzRIBAHhgEXYBB8uXL1+y2/8eO3ZMklS3bt1k7d3c3HKjLAAADIGwCzhYlSpVtGvXriTbduzYIZPJpEaNGiVrf+3atXTvVAQAABIQdgEH69Gjh/z9/fXaa6/p1KlTWrt2rRYtWiR3d3d16NAhWfsDBw6ocuXKDqgUAIAHj4ujCwAedqNGjdLPP/+s77//XosWLZKUsAj7Rx99lGzIwtGjR3X+/Hk999xzDqgUAIAHD2EXcLBChQpp69atmjdvng4dOqSiRYuqV69e6ty5c7K2x48fV5cuXdSlSxcHVAoAwIOHm0oAAADAsBizCwAAAMMi7AIAAMCwCLsAAAAwLMIuAAAADIuwC8CwfH19ZTabVatWrVw9NrfZo9auXbvKbDbrp59+yrFzPkhGjRols9ms6dOn59o1H6TnHPAgIewCyDXBwcGaPn262rdvr/Lly8vLy0tVq1ZV06ZNNWzYMC1YsEABAQGOLjNP8fX11fTp07VhwwZHlyJJunTpkqZPn6558+bl+rWtATw3AyiABx/r7ALIFX/88Yf69++vW7duSZK8vb1VqVIlxcXF6cKFC/rrr7+0Zs0ahYSEaMKECQ6uNmH942rVqqlUqVIOrWPPnj2aOXOmnnnmGXXr1i3FNvaotWzZsqpWrZo8PDySbL98+bJmzpypcuXKafTo0Tl2PQCwF8IuALsLDw/X4MGDdevWLTVo0ED/+c9/VK9ePdv++Ph4HT16VKtXr5bZbHZcoYnUr19ff/zxh6PLyBB71Pr111/n6PkAwFEIuwDsbtu2bbp+/bqcnZ31448/qmTJkkn2Ozk5qX79+qpfv76DKgQAGBVjdgHY3YULFyRJnp6eyYJuejIyaSejk4l++ukntW/fXmXLllW5cuXUvXt3/fbbb1m+7u7duzV06FA9+uijKl68uCpVqqQ+ffpo48aNadZx/fp1TZ48WS1atFC5cuVUqlQp1atXT88//7w2b95sa2c2mzVz5kxJ0tKlS2U2m5N8pVVrZGSkypUrJ7PZrAMHDqRay99//y2z2SxPT08FBQXZtqc0Qa1r167q3r27JCkgICBZPb6+vvrll19kNptVs2ZNxcfHp3rdjz76SGazWf369Uvz3yq7/vnnH82ePVvdunVTzZo15e3trfLly+vJJ5/UV199pXv37qV7jpCQEE2aNEm1a9dWiRIl9Oijj+rVV1/VtWvX0jwuq88PADmLsAvA7goXLixJunHjhvz9/R1SwzvvvKMxY8YoICBA1apVk7Ozs3x9ffX000/riy++yNS5LBaLJk6cqB49emjt2rWKiorSo48+qnz58mnHjh0aNGhQquOOf/vtNzVq1Ehz5szR6dOnVbp0aVWvXl23b9/Wzz//rIkTJ9raNmnSRGXLlpUkFS9eXE2aNEnylZZChQrZgumyZctSbWfd165dO3l7e6d5Th8fH/n4+EiS8ufPn6weDw8Pde3aVSVKlNCVK1e0ffv2FM8TFxenJUuWSJKGDBmS5jWza8qUKfrggw907Ngxubq66rHHHlORIkV08OBBvfnmm+rdu3eagTckJETt2rXT/PnzVahQIVWvXl03btzQokWL1KpVK507dy7ZMdl5fgDIeYRdAHb3xBNPyNnZWZLUu3dvfffdd7py5UquXf/atWv66quvNHv2bJ09e1a///67/vnnH1vgeP/99zM15vWzzz7T/PnzVaZMGS1btkwXL17U7t27de7cOa1evVrFixfXggULkoXMM2fOaMiQIQoNDVXPnj31559/6sCBA9q1a5cuXLig/fv3a/jw4bb2W7Zs0aBBgyRJHTp00JYtW5J8pWfAgAGSpDVr1uju3bvJ9lssFq1YsUKS9Mwzz6R7vo8//tjW01yiRIlk9dSpU0f58uWz1bx48eIUz/Pbb7/p6tWr8vb2VqdOndK9bnb0799f27dvV0BAgI4cOaIdO3bo5MmTOnjwoBo2bKi9e/dq7ty5qR7/7bffSpL8/Py0f/9+7dmzR8ePH1f9+vV18+ZNDRs2THFxcUmOyerzA4B9EHYB2F3FihU1bdo0OTk56fLly3rttddUs2ZNVa9eXX379tWnn35q1x7f2NhYPfvssxo2bJhMJpMkycXFRe+8847atm2r+Ph4zZo1K0PnCgkJ0ccff2wbf3x/WGvfvr3tXLNnz06y76OPPlJkZKSaN2+u7777LtnqCY888ojGjRuXxUeZXMuWLVW2bFmFhISkGI737NmjgIAAeXh4qEuXLjl23aFDh8pkMmnLli26efNmsv3WEDxo0CC5uNh36kjXrl1Vv3592/+7VfXq1W2T8JYuXZrq8TExMfryyy/16KOP2raVLVtW3333nVxcXPTnn38mGZaQnecHAPsg7ALIFSNHjtSOHTvUt2/fJMMatm3bpilTpqhBgwYaPXq0IiIi7HL9UaNGpbl9586diomJSfc8v/76q8LDw1W3bl3VrVs3xTadO3dWvnz5dPbsWV2/fl2SFB0drV9//VWS9Prrr8vJyf5vvyaTSf3795eUcqBbvny5pITe9gIFCuTYdStWrKi2bdsqJiYm2XWDgoK0detWmUwmDR48OMeumZabN2/qq6++0ogRI9SrVy917txZnTp1si2d9vfffysqKirFY+vVq6fGjRsn216+fHnbUnDW/1fr37Py/ABgP6zGACDXPP7441qwYIHi4uJ0+vRpHT9+XL6+vvr11191+/ZtLVmyRMHBwbYQllNcXFxUrVq1FPc98sgjkhLC6KVLl1S1atU0z3Xq1ClJCTdXSOsjeGtP4tWrV1WyZEn5+/vbhhI0atQo048hqwYMGKBZs2Zp+/bt+vfff+Xl5SVJioqK0rp162xtctpzzz2nHTt26Mcff9TYsWNt25csWaLY2Fi1atVKlSpVyvHr3m/t2rUaM2aMwsPDU21jsVh0+/ZtFSxYMNm+xD2697M+dxKP283q8wOA/RB2AeQ6Z2dn1apVS7Vq1dKzzz6rO3fuaMyYMdqwYYO2bt2qP/74Qw0bNsyx63l6etrGDN+vRIkStr+nFYisQkJCJCX0Fqb0Ef39IiMjJUlhYWGSEh67u7t7usfllGrVqqlBgwY6dOiQVq1apZdeekmStGnTJoWGhqpSpUpq2rRpjl+3S5cuKlmypM6dO6d9+/bZrvHjjz9KShjqYG+XLl3SiBEjdPfuXfXu3VsjR45U9erV5eHhIRcXF8XHx6tYsWKSlGqvfuLnR2r7Ej9vsvr8AGA/DGMA4HBFihTR3LlzbR/tJ54sZu0Bs1gsqR6fXmAIDg5ONonI6saNG7a/ZySEurm5SUroDQ0JCUn3q2XLlpL+tyJFXFxchkJ1TrL23CaeEGX9u3WYQ05zcXHRs88+K+l/Y3R9fX3l7++vYsWKpXo3uJz0888/6+7du6pfv74WLlyoJk2aqFixYrZxwta7+aUl8fMjtX2JnzdZfX4AsB/CLoA8oUiRIraP2BP3slnDw7///pvqsf/880+a546NjU21zZkzZyRJBQoUUIUKFdKt07r01p9//plu28SqVq1qGxd78ODBDB93/8SqrHjqqafk6uqqY8eO6cyZM7px44Z27Nghk8mU6SEMmalnyJAhcnJy0tq1axUaGqoffvhBUkLAzp8/f6aumxWXLl2SlLCEW0pjpDOyAof1+ZHWvurVq9u2ZfX5AcB+CLsA7C44ODjNGwxICZOErB/7VqlSxba9UqVKMplMio6O1vHjx5Mdt3///gwFi6+++irN7W3atFG+fPnSPU+nTp1UsGBBnTx5Ur///nu67a3y58+vjh07SkqYhZ9WT3VihQoVkqRUJ1BlRNGiRfXkk09KSpiUtnLlSsXFxalJkyaqWLFips6VmXrKly+v9u3bKzIyUt98841tjHBuDGGQZBuDm/hmGVYWi0Wff/55uuc4fPhwiqE4ICDAtgqD9d9WyvrzA4D9EHYB2N3q1avVpEkTffnll7p69WqSfRaLRdu3b9fAgQNlsVhUrlw5tW/f3rbfbDbbxnu++eabST56Pn78uF566aV0Q6qLi4sWL16sRYsW2UJmbGysZsyYoR07dsjJyUnjx4/P0GMpXry43njjDUkJoW3p0qWKjY1N0ub27dtaunSp3nvvvSTb3377bRUqVEi+vr4aPnx4spn4Z86c0Zw5c5Jss07iOnToULaGP1h7cFesWGEbwpCRtXXvZ/3l499//7VNxkrLc889J0maNm2aoqOj1bhxY9vELntr3ry5JOmXX37R1q1bbdvDwsL0yiuv6MiRI+meI1++fBo1apTOnj1r23b16lU9//zziomJkY+PT5Jl27Lz/ABgH0xQA2B3JpNJ586d01tvvaW33npL3t7eKlWqlGJiYnT16lXbpJ6SJUvqxx9/TDYrfurUqeratav27dsnHx8fVa1aVVFRUfL391f79u3VqFEj280RUlKqVCn16NFDr776qqZNm6YyZcrowoULun37tiTp//7v/zK1QsL48eN1584dffbZZxo1apQmTJigKlWqyMXFRTdu3NCVK1dksVhsYcuqRo0aWrx4sYYNG6aff/5Zv/zyi6pXr678+fMrICBAt27dUrly5ZKstduuXTvbHckee+wxVatWzTYEIDO3ne3YsaOKFSumq1ev6urVqypYsKB69eqV4eOtrL3EW7duVbt27fTII4/YxiNPnz5dtWvXTtK+U6dOKl26tAIDAyXlzB3TPvvsMy1YsCDV/WXLltXu3bvVpUsXtWjRQnv27FH//v1VoUIFFS1aVOfOnVN0dLTmzZtnm7CXmueff17btm1TkyZN9Mgjj8jFxUV//fWXYmNj5eXlpYULFyZbKzirzw8A9kHPLgC7GzZsmDZt2qQJEyaoWbNmkhLGNP79999ydXVVq1at9OGHH+qPP/5QnTp1kh1fv359bdmyRR07dlT+/Pn1zz//yNXVVVOmTNHy5ctTXWkhsY8++khffPGFypQpo3PnzikmJkYtWrTQihUr9Oqrr2bq8ZhMJk2ZMsV269fixYvr7NmzOnHihGJjY9W+fXv95z//0fz585Md26FDBx04cEBjxoxR9erVdfnyZf3zzz8ym83q27dvsptbuLm5ae3aterRo4cKFCigY8eOae/evdq7d2+mas6XL5+eeuop2/ddunSRh4dHps5h9fXXX+vFF19U6dKldebMGVs9d+7cSdbW2dlZAwcOlCR5eHiod+/eWbpmYlFRUbp161aqX9ZfYpycnLRy5Uq99tprqlChggIDA3XlyhW1bNlS69aty9B4ZbPZrB07dmjEiBEKDw/X2bNn5eXlpcGDB2vXrl0pLk2WnecHgJxnCgkJydjAMQB4iOzatUs9e/ZUpUqVdPToUUeX80B79dVXtWjRIj3//PP69NNPHV0OgIcMPbsAkALr0IqiRYs6tpAHXGhoqFavXi0p9yamAUBihF0AuE98fLzWr18vSSkOq0DGTZ8+XeHh4WrWrBn/lgAcgglqAJDI4MGDdfjwYQUGBsrFxUXDhg1zdEkPnN9++02zZ89WYGCgLly4IGdnZ02ZMsXRZQF4SNGzCwCJHD9+XCEhIWrSpIlWrVqVbHUBpC8oKEh79+7VtWvX9Pjjj2vp0qVq0KCBo8sC8JBighoAAAAMi55dAAAAGBZhFwAAAIZF2AUAAIBhEXYBAABgWIRdAAAAGBZhFwAAAIZF2AUAAIBhEXYBAABgWP8P6QsvVa6a4IYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,5))\n",
    "color = ['green','red']\n",
    "df['Subjectivity Label'].value_counts().plot(kind='bar',color = color)\n",
    "plt.title('Subjectivity Among The Review')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Subjectivity Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2fcc04fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Word Count']);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}