{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenization adn Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\felix\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\felix\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\felix\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nltk count words\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "# Load the data from the JSON file\n",
    "data_path = './data/reddit_jokes.json'\n",
    "with open(data_path) as f:\n",
    "    data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>body</th>\n",
       "      <th>id</th>\n",
       "      <th>score</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Now I have to say \"Leroy can you please paint ...</td>\n",
       "      <td>5tz52q</td>\n",
       "      <td>1</td>\n",
       "      <td>I hate how you cant even say black paint anymore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pizza doesn't scream when you put it in the ov...</td>\n",
       "      <td>5tz4dd</td>\n",
       "      <td>0</td>\n",
       "      <td>What's the difference between a Jew in Nazi Ge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>...and being there really helped me learn abou...</td>\n",
       "      <td>5tz319</td>\n",
       "      <td>0</td>\n",
       "      <td>I recently went to America....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Sunday school teacher is concerned that his ...</td>\n",
       "      <td>5tz2wj</td>\n",
       "      <td>1</td>\n",
       "      <td>Brian raises his hand and says, “He’s in Heaven.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>He got caught trying to sell the two books to ...</td>\n",
       "      <td>5tz1pc</td>\n",
       "      <td>0</td>\n",
       "      <td>You hear about the University book store worke...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                body      id  score  \\\n",
       "0  Now I have to say \"Leroy can you please paint ...  5tz52q      1   \n",
       "1  Pizza doesn't scream when you put it in the ov...  5tz4dd      0   \n",
       "2  ...and being there really helped me learn abou...  5tz319      0   \n",
       "3  A Sunday school teacher is concerned that his ...  5tz2wj      1   \n",
       "4  He got caught trying to sell the two books to ...  5tz1pc      0   \n",
       "\n",
       "                                               title  \n",
       "0   I hate how you cant even say black paint anymore  \n",
       "1  What's the difference between a Jew in Nazi Ge...  \n",
       "2                     I recently went to America....  \n",
       "3  Brian raises his hand and says, “He’s in Heaven.”  \n",
       "4  You hear about the University book store worke...  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create pandas dataframe of the data\n",
    "df = pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: bit more than 1000 jokes for removing duplicates and empty jokes\n",
    "num_good = 1033\n",
    "num_bad = 1019\n",
    "min_score = 50\n",
    "\n",
    "\n",
    "df_sroted = df.sort_values(by='score', ascending=False)\n",
    "df_sroted = df_sroted.reset_index(drop=True)\n",
    "\n",
    "df_good = df_sroted.head(num_good)\n",
    "df_good = df_good.reset_index(drop=True)\n",
    "\n",
    "df_bad = df_sroted[df_sroted['score'] > min_score].tail(num_bad)\n",
    "df_bad = df_bad.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg score good jokes: 10802.19 shape: (1033, 4)\n",
      "avg score bad jokes: 52.3 shape: (1019, 4)\n"
     ]
    }
   ],
   "source": [
    "# avg score and shape\n",
    "print('avg score good jokes:', df_good['score'].mean().round(2), 'shape:', df_good.shape)\n",
    "print('avg score bad jokes:', df_bad['score'].mean().round(2), 'shape:', df_bad.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "####################################################################################################\n",
      "5 random good jokes:\n",
      "####################################################################################################\n",
      "George R R Martin, dead after reaching peak popularity\n",
      "[Removed]\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Royal Wedding\n",
      "My dad was holding me from behind .\n",
      "----------------------------------------------------------------------------------------------------\n",
      "I want to be a millionaire just like my dad!!\n",
      "...The King of England and The King of Spain are having an argument over who has the biggest penis. Eventually they decide to let the people judge.\n",
      "They all stand on a stage in front of the people and drop their pants one by one.\n",
      "\n",
      "The king of France drops his and the French crowd shout \"viva la france!!\"\n",
      "\n",
      "The king of Spain drops his and the Spanish crowd shout \"Viva la españa!!\"\n",
      "\n",
      "The king of England drops his, a long silence from the crowd, and then everybody shouts \"God save the Queen!!!\"\n",
      "\n",
      "\n",
      "\n",
      "Edit: I posted this whilst high, The title of the post is part of the joke the ellipses symbolise this \n",
      "----------------------------------------------------------------------------------------------------\n",
      "A women is cooking eggs in the kitchen when her husband comes running in…\n",
      "You can hide, but you can't run.\n",
      "----------------------------------------------------------------------------------------------------\n",
      "You know that tingly sensation you get when you like somebody?\n",
      "...and sees a gorgeous woman nursing a drink. Walking up behind her he says: \"Hi there, good lookin'. How's it going?\"\n",
      "Having already downed a few power drinks, she turns around, faces him, looks him straight in the eye and says: \"Listen up, buddy. I screw anybody, anytime, anywhere, your place, my place, in the car, front door,back door, on the ground, standing up, sitting down, naked or with clothes on,dirty, clean... It just doesn't matter to me. I've been doing it ever since I got out of college and I just flat-ass love it.\"\n",
      "Eyes now wide with interest, he responds: \"No kidding. I'm a lawyer too. What firm are you with?\"\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# print random 5 good jokes\n",
    "print('#' * 100)\n",
    "print('5 random good jokes:')\n",
    "print('#' * 100)\n",
    "for i in range(5):\n",
    "    print(df_good['title'][np.random.randint(0, num_good)])\n",
    "    print(df_good['body'][np.random.randint(0, num_good)])\n",
    "    print('-' * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "####################################################################################################\n",
      "5 random bad jokes:\n",
      "####################################################################################################\n",
      "Saying Goodbye to Mother\n",
      "There was a farmer who had a brown cow and a white cow and he wanted to get them bred, so borrowed his neighbor's bull and turned it loose in the pasture. He told his son to watch and come in and tell him when the bull was finished.\n",
      "\n",
      "After a while the boy came into the living where his father was talking with some friends. \"Say, Pop,\" said the boy. \"Yes,\" replied his father.\n",
      "\n",
      "\"The bull just fucked the brown cow.\"\n",
      "\n",
      "There was a sudden lull in the conversation. The father said \"Excuse me\" and took his son outside. \"Son, you mustn't use language like that in front of company. You should say 'The bull surprised the brown cow'. Now go and watch and tell me when the bull surprises the white cow.\"\n",
      "\n",
      "The father went back inside the house. After a while the boy came in and said, \"Hey, Daddy.\"\n",
      "\n",
      "\"Yes, son. Did the bull surprise the white cow?\"\n",
      "\n",
      "\"He sure did, Pop! He fucked the brown cow again!\"\n",
      "\n",
      "----------------------------------------------------------------------------------------------------\n",
      "I organized a threesome this past weekend.\n",
      "But now I am a well-rounded person, so it worked out pretty well.\n",
      "----------------------------------------------------------------------------------------------------\n",
      "A guy gets his bike stolen from synagogue...\n",
      "Eclipse it!\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Why are Hispanic dwarves called Paragraphs?\n",
      "Seriously. That other guy hasn't answered yet, and I'm dying to find out!\n",
      "----------------------------------------------------------------------------------------------------\n",
      "What do a burnt pizza, a frozen beer, and a pregnant woman have in common?\n",
      "They steal all the green cards.\n",
      "\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# print random 5 bad jokes\n",
    "print('#' * 100)\n",
    "print('5 random bad jokes:')\n",
    "print('#' * 100)\n",
    "for i in range(5):\n",
    "    print(df_bad['title'][np.random.randint(0, num_bad)])\n",
    "    print(df_bad['body'][np.random.randint(0, num_bad)])\n",
    "    print('-' * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape after tokenizing: (1033, 7) (1019, 7)\n",
      "shape after removing empty jokes: (1033, 7) (1019, 7)\n",
      "shape after removing duplicates: (1002, 7) (1000, 7)\n"
     ]
    }
   ],
   "source": [
    "bool_lemma = True\n",
    "\n",
    "# tokenize the jokes\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "def tokenize_and_lemmatize_joke(joke):\n",
    "    tokens = word_tokenize(joke)\n",
    "    tokens = [word.lower() for word in tokens if word.isalpha()]\n",
    "    tokens = [word for word in tokens if word not in stop_words]\n",
    "    if bool_lemma:\n",
    "        tokens = [lemmatizer.lemmatize(word) for word in tokens]\n",
    "    return tokens\n",
    "\n",
    "# Tokenize and lemmatize the jokes\n",
    "df_good['tok_body'] = df_good['body'].apply(tokenize_and_lemmatize_joke)\n",
    "df_bad['tok_body'] = df_bad['body'].apply(tokenize_and_lemmatize_joke)\n",
    "\n",
    "df_good['tok_title'] = df_good['title'].apply(tokenize_and_lemmatize_joke)\n",
    "df_bad['tok_title'] = df_bad['title'].apply(tokenize_and_lemmatize_joke)\n",
    "\n",
    "df_good['tokens'] = df_good['tok_body'] + df_good['tok_title']\n",
    "df_bad['tokens'] = df_bad['tok_body'] + df_bad['tok_title']\n",
    "\n",
    "\n",
    "print('shape after tokenizing:', df_good.shape, df_bad.shape)\n",
    "# remove empty jokes\n",
    "df_good = df_good[df_good['tokens'].map(len) > 0]\n",
    "df_bad = df_bad[df_bad['tokens'].map(len) > 0]\n",
    "print('shape after removing empty jokes:', df_good.shape, df_bad.shape)\n",
    "\n",
    "# remove duplicates\n",
    "df_good = df_good.drop_duplicates(subset='body')\n",
    "df_bad = df_bad.drop_duplicates(subset='body')\n",
    "print('shape after removing duplicates:', df_good.shape, df_bad.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good words: Counter({'say': 523, 'man': 401, 'said': 393, 'one': 309, 'get': 252, 'go': 238, 'woman': 203, 'wife': 188, 'guy': 187, 'like': 169, 'asked': 164, 'back': 164, 'know': 163, 'would': 162, 'asks': 162, 'day': 160, 'time': 153, 'girl': 147, 'husband': 142, 'well': 141, 'two': 138, 'edit': 138, 'want': 137, 'see': 134, 'old': 129, 'think': 127, 'joke': 126, 'take': 125, 'could': 124, 'look': 122, 'first': 119, 'year': 118, 'give': 116, 'told': 112, 'got': 110, 'come': 109, 'people': 108, 'little': 105, 'went': 104, 'boy': 104, 'going': 99, 'next': 98, 'tell': 98, 'father': 98, 'sex': 97, 'walk': 96, 'good': 93, 'bar': 93, 'son': 92, 'make': 90, 'friend': 84, 'replied': 82, 'dad': 82, 'hand': 81, 'find': 80, 'reply': 79, 'ca': 78, 'way': 78, 'never': 76, 'really': 75, 'minute': 75, 'home': 71, 'right': 71, 'around': 71, 'doctor': 69, 'car': 68, 'put': 67, 'still': 66, 'night': 66, 'later': 66, 'blonde': 65, 'black': 65, 'priest': 64, 'god': 63, 'much': 63, 'new': 63, 'house': 63, 'let': 62, 'front': 62, 'door': 62, 'lady': 61, 'girlfriend': 60, 'oh': 60, 'second': 60, 'every': 59, 'last': 59, 'thing': 58, 'dollar': 58, 'sure': 58, 'drink': 57, 'came': 57, 'eye': 57, 'room': 57, 'thought': 56, 'kid': 56, 'yes': 56, 'three': 55, 'ever': 54, 'head': 54, 'bartender': 54, 'nothing': 53, 'call': 53, 'today': 52, 'word': 51, 'finally': 51, 'mother': 51, 'took': 51, 'thanks': 50, 'turn': 50, 'hell': 50, 'job': 49, 'men': 49, 'ask': 49, 'another': 48, 'even': 48, 'couple': 48, 'officer': 48, 'line': 48, 'teacher': 47, 'bed': 46, 'started': 46, 'heard': 46, 'difference': 46, 'please': 45, 'left': 45, 'great': 45, 'sitting': 45, 'work': 44, 'many': 44, 'everyone': 43, 'start': 43, 'life': 43, 'best': 43, 'love': 42, 'trump': 41, 'need': 41, 'sir': 41, 'must': 41, 'found': 40, 'long': 40, 'show': 40, 'answer': 40, 'satan': 40, 'better': 39, 'try': 39, 'mean': 39, 'open': 39, 'big': 38, 'young': 38, 'wow': 38, 'driver': 38, 'lawyer': 38, 'looked': 37, 'gold': 37, 'help': 37, 'question': 37, 'mom': 36, 'bet': 36, 'anything': 36, 'president': 35, 'wrong': 35, 'world': 35, 'getting': 35, 'enough': 35, 'week': 35, 'use': 35, 'money': 35, 'change': 35, 'arm': 34, 'away': 34, 'pull': 34, 'morning': 34, 'married': 34, 'bit': 34, 'also': 33, 'saying': 33, 'child': 33, 'white': 33, 'nice': 33, 'wanted': 33, 'face': 33, 'always': 33, 'u': 33, 'beer': 33, 'sorry': 32, 'order': 32, 'ok': 32, 'keep': 32, 'page': 32, 'fuck': 32, 'called': 32, 'owner': 32, 'fucking': 32, 'made': 31, 'without': 31, 'thank': 31, 'immediately': 31, 'hot': 31, 'happy': 31, 'na': 31, 'shit': 31, 'name': 31, 'grandpa': 31, 'place': 30, 'since': 30, 'free': 30, 'student': 30, 'behind': 30, 'stop': 30, 'something': 30, 'talk': 30, 'end': 30, 'pulled': 29, 'step': 29, 'school': 29, 'water': 29, 'bedroom': 29, 'lot': 29, 'table': 29, 'responds': 29, 'wo': 28, 'jew': 28, 'happened': 28, 'police': 28, 'buy': 28, 'check': 28, 'light': 28, 'looking': 28, 'someone': 28, 'run': 28, 'engineer': 28, 'cop': 28, 'bill': 27, 'problem': 27, 'smile': 27, 'saw': 27, 'turned': 27, 'leaf': 27, 'third': 27, 'penis': 27, 'beggar': 27, 'class': 27, 'wall': 26, 'person': 26, 'tried': 26, 'daughter': 26, 'family': 26, 'leg': 26, 'sound': 26, 'pretty': 26, 'beautiful': 26, 'baby': 26, 'dinner': 26, 'trying': 26, 'hey': 26, 'five': 26, 'ten': 25, 'course': 25, 'bad': 25, 'together': 25, 'hole': 25, 'feel': 25, 'suit': 25, 'devil': 25, 'dead': 25, 'tree': 25, 'drug': 25, 'hear': 24, 'seen': 24, 'month': 24, 'watch': 24, 'gay': 24, 'kind': 24, 'parent': 24, 'post': 24, 'floor': 24, 'ground': 24, 'john': 24, 'ran': 23, 'glass': 23, 'hit': 23, 'irishman': 23, 'office': 23, 'frog': 23, 'muslim': 23, 'gate': 22, 'pay': 22, 'everything': 22, 'laugh': 22, 'may': 22, 'drinking': 22, 'decides': 22, 'air': 22, 'wish': 22, 'fell': 22, 'yeah': 22, 'dick': 22, 'peter': 22, 'okay': 22, 'coming': 22, 'nbsp': 22, 'donald': 21, 'sleep': 21, 'body': 21, 'hour': 21, 'point': 21, 'side': 21, 'decided': 21, 'number': 21, 'done': 21, 'suddenly': 21, 'knock': 21, 'naked': 21, 'standing': 21, 'leave': 21, 'fall': 21, 'prostitute': 21, 'bob': 21, 'window': 20, 'jewish': 20, 'moment': 20, 'sent': 20, 'guess': 20, 'mouth': 20, 'sister': 20, 'drunk': 20, 'game': 20, 'mine': 20, 'gon': 20, 'group': 20, 'ear': 20, 'lost': 20, 'jesus': 20, 'bos': 20, 'able': 19, 'met': 19, 'died': 19, 'walking': 19, 'cry': 19, 'four': 19, 'several': 19, 'honey': 19, 'wearing': 19, 'state': 19, 'stand': 19, 'pant': 19, 'outside': 19, 'american': 19, 'age': 19, 'pocket': 19, 'box': 19, 'dog': 19, 'pub': 19, 'hard': 19, 'voice': 19, 'funny': 19, 'finger': 19, 'ye': 19, 'favorite': 18, 'responded': 18, 'phone': 18, 'sits': 18, 'however': 18, 'top': 18, 'dirty': 18, 'whisper': 18, 'die': 18, 'nsfw': 18, 'believe': 18, 'shot': 18, 'shop': 18, 'thinking': 18, 'drop': 18, 'idea': 18, 'ago': 18, 'star': 18, 'texan': 18, 'king': 18, 'least': 17, 'live': 17, 'fine': 17, 'yet': 17, 'whole': 17, 'foot': 17, 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1, 'damon': 1, 'jackman': 1, 'planning': 1, 'ww': 1, 'iii': 1, 'penne': 1, 'pun': 1, 'easiest': 1, 'gum': 1, 'hereditary': 1, 'heading': 1, 'progress': 1, 'principal': 1, 'custodian': 1, 'demonstrate': 1, 'squeegee': 1, 'dipped': 1, 'cleaned': 1, 'continuing': 1, 'create': 1, 'sheen': 1, 'roughly': 1, 'nugent': 1, 'slut': 1, 'cumference': 1, 'asexual': 1, 'citation': 1, 'cryin': 1, 'bump': 1, 'bostonian': 1, 'pahks': 1, 'cah': 1, 'yahd': 1, 'texan': 1, 'warshes': 1, 'conservation': 1, 'r': 1, 'newly': 1, 'graduated': 1, 'deranged': 1, 'piggy': 1, 'procrastinator': 1, 'punchline': 1, 'bluff': 1, 'drama': 1, 'hollywood': 1, 'sceptical': 1, 'retort': 1, 'shaken': 1, 'express': 1, 'implores': 1, 'assembled': 1, 'among': 1, 'disappears': 1, 'headed': 1, 'emerges': 1, 'paramedic': 1, 'straw': 1, 'texas': 1, 'inspect': 1, 'illegally': 1, 'grown': 1, 'location': 1, 'verbally': 1, 'exploded': 1, 'mister': 1, 'arrogant': 1, 'displayed': 1, 'wherever': 1, 'kindly': 1, 'gertrudis': 1, 'gaining': 1, 'gored': 1, 'safety': 1, 'terrified': 1, 'divorced': 1, 'gentle': 1, 'function': 1, 'diagnostically': 1, 'telemarketing': 1, 'process': 1, 'implement': 1, 'method': 1, 'administration': 1, 'marketing': 1, 'gynecologist': 1, 'collector': 1, 'brudder': 1, 'em': 1, 'wurst': 1, 'arrange': 1, 'depend': 1, 'circumstance': 1, 'infidelity': 1, 'stereo': 1, 'proof': 1, 'poison': 1, 'remover': 1, 'donating': 1, 'chickpea': 1, 'hewbrews': 1, 'moses': 1, 'unholstered': 1, 'model': 1, 'magazine': 1, 'chamber': 1, 'ammo': 1, 'marlboro': 1, 'brownie': 1, 'cub': 1, 'compared': 1, 'vinegar': 1, 'stored': 1, 'boring': 1, 'dentist': 1, 'text': 1, 'daybreak': 1, 'scatter': 1, 'leisure': 1, 'whatcha': 1, 'mutter': 1, 'bite': 1, 'duckling': 1, 'welp': 1, 'proved': 1, 'shame': 1, 'grandson': 1, 'sunrise': 1, 'bundle': 1, 'scurry': 1, 'dusk': 1, 'fallow': 1, 'item': 1, 'unknown': 1, 'pussywillow': 1, 'jolt': 1, 'blame': 1, 'cant': 1, 'didnt': 1, 'inertia': 1, 'wooded': 1, 'nickel': 1, 'accepted': 1, 'backside': 1, 'flustered': 1, 'cornhole': 1, 'facilitated': 1, 'maintain': 1, 'deductive': 1, 'mowed': 1, 'lawn': 1, 'marvellous': 1, 'demonstration': 1, 'fermat': 1, 'theorem': 1, 'chunk': 1, 'grandad': 1, 'wike': 1, 'mufasa': 1, 'simba': 1, 'cloning': 1, 'treatment': 1, 'earn': 1, 'restoring': 1, 'charger': 1, 'bernadette': 1, 'floating': 1, 'gal': 1, 'owe': 1, 'contestant': 1, 'slash': 1, 'visible': 1, 'fatal': 1, 'impression': 1, 'extractor': 1, 'armoe': 1, 'takin': 1, 'burned': 1, 'stove': 1, 'unwrapped': 1, 'rust': 1, 'layer': 1, 'gel': 1, 'stacked': 1, 'dining': 1, 'awkward': 1, 'spatter': 1, 'salesman': 1, 'presidency': 1, 'ethiopian': 1, 'cellar': 1, 'intercom': 1, 'forever': 1, 'la': 1, 'salty': 1, 'freaky': 1, 'uncomfortable': 1, 'predominantly': 1, 'spite': 1, 'flasher': 1, 'retiring': 1, 'rap': 1, 'song': 1, 'tortilla': 1, 'thermostat': 1, 'thermos': 1, 'jackson': 1, 'pollock': 1, 'rifle': 1, 'luckily': 1, 'purpose': 1, 'incredibly': 1, 'violin': 1, 'virtuoso': 1, 'vexed': 1, 'fiddling': 1, 'confusion': 1, 'pajama': 1, 'talker': 1, 'rambling': 1, 'normally': 1, 'stumbling': 1, 'wont': 1, 'un': 1, 'convention': 1, 'kirk': 1, 'chekhov': 1, 'scotty': 1, 'uhura': 1, 'sulu': 1, 'iraqi': 1, 'afghan': 1, 'egyptian': 1, 'palestinian': 1, 'saudi': 1, 'syrian': 1, 'shih': 1, 'tzu': 1, 'cheapest': 1, 'forlorn': 1, 'kinda': 1, 'overjoyed': 1, 'gallop': 1, 'hurtling': 1, 'bounding': 1, 'dune': 1, 'zooming': 1, 'oasis': 1, 'gapping': 1, 'charging': 1, 'tugging': 1, 'rein': 1, 'amen': 1, 'dinklage': 1, 'youtube': 1, 'frederikson': 1, 'similar': 1, 'agony': 1, 'dirtbag': 1, 'hoover': 1, 'drumpf': 1, 'backstory': 1, 'brief': 1, 'overview': 1, 'physiological': 1, 'theory': 1, 'description': 1, 'doctorate': 1, 'studying': 1, 'positive': 1, 'reinforcement': 1, 'treat': 1, 'sardine': 1, 'overnight': 1, 'habitat': 1, 'leap': 1, 'scaring': 1, 'conducted': 1, 'receptive': 1, 'correlation': 1, 'susceptibility': 1, 'snuck': 1, 'flashlight': 1, 'shone': 1, 'youre': 1, 'transporting': 1, 'underage': 1, 'gull': 1, 'sedate': 1, 'unorthodox': 1, 'palmed': 1, 'secretly': 1, 'loved': 1, 'lug': 1, 'hammering': 1, 'fairly': 1, 'stagger': 1, 'sending': 1, 'trot': 1, 'squealing': 1, 'murder': 1, 'drunkenly': 1, 'mein': 1, 'kraft': 1, 'easymac': 1, 'feely': 1, 'afterall': 1, 'nosy': 1, 'diary': 1, 'forgot': 1, 'burnt': 1, 'frozen': 1, 'requires': 1, 'ungrateful': 1, 'solved': 1, 'stutterer': 1, 'mark': 1, 'unhappy': 1, 'monitor': 1, 'brag': 1, 'multiple': 1, 'organism': 1, 'laboratory': 1, 'umbrella': 1, 'endurance': 1, 'applauded': 1, 'medium': 1, 'independent': 1, 'loyal': 1, 'follower': 1, 'dote': 1, 'infantry': 1, 'spied': 1, 'sunken': 1, 'gorged': 1, 'attacking': 1})\n",
      "avg relative frequency difference: 0.04720864704418073\n"
     ]
    }
   ],
   "source": [
    "# count the words\n",
    "good_words = Counter()\n",
    "for tokens in df_good['tokens']:\n",
    "    good_words.update(tokens)\n",
    "\n",
    "bad_words = Counter()\n",
    "for tokens in df_bad['tokens']:\n",
    "    bad_words.update(tokens)\n",
    "\n",
    "print('good words:', good_words)\n",
    "print('bad words:', bad_words)\n",
    "\n",
    "# if words in both good and bad jokes\n",
    "common_words = good_words.keys() & bad_words.keys()\n",
    "# plot counter of common words for good and bad jokes\n",
    "common_words_good = {word: good_words[word] for word in common_words}\n",
    "common_words_bad = {word: bad_words[word] for word in common_words}\n",
    "# realtive how often word is used in good and bad jokes\n",
    "common_words_good_rel = {word: good_words[word] / (good_words[word] + bad_words[word]) for word in common_words}\n",
    "common_words_bad_rel = {word: bad_words[word] / (good_words[word] + bad_words[word]) for word in common_words}\n",
    "# avg relative frequency difference between good and bad jokes\n",
    "avg_rel_diff = np.mean([common_words_good_rel[word] - common_words_bad_rel[word] for word in common_words])\n",
    "print('avg relative frequency difference:', avg_rel_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count length of jokes\n",
    "df_good['length'] = df_good['tokens'].apply(len)\n",
    "df_bad['length'] = df_bad['tokens'].apply(len)\n",
    "\n",
    "# plot the length of jokes\n",
    "plt.hist(df_good['length'], bins=20, alpha=0.5, label='good jokes')\n",
    "plt.hist(df_bad['length'], bins=20, alpha=0.5, label='bad jokes')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of words')\n",
    "plt.ylabel('Number of jokes')\n",
    "plt.title('Length of jokes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot length and rating of jokes\n",
    "plt.scatter(df_good['length'], df_good['score'], alpha=0.5, label='good jokes')\n",
    "plt.scatter(df_bad['length'], df_bad['score'], alpha=0.5, label='bad jokes')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of words')\n",
    "plt.ylabel('Rating')\n",
    "plt.title('Length and rating of jokes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "####################################################################################################\n",
      "5 random good jokes:\n",
      "####################################################################################################\n",
      "['one', 'buy', 'going', 'kill', 'writing', 'book', 'called', 'overreacting']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "['christian', 'priest', 'came', 'laid', 'hand', 'hand', 'said', 'jesus', 'christ', 'walk', 'today', 'smiled', 'told', 'paralysed', 'rabbi', 'came', 'laid', 'hand', 'hand', 'said', 'god', 'almighty', 'walk', 'today', 'le', 'amused', 'told', 'nothing', 'wrong', 'mullah', 'came', 'took', 'hand', 'said', 'insha', 'allah', 'walk', 'today', 'snapped', 'nothing', 'wrong', 'buddhist', 'monk', 'came', 'held', 'hand', 'said', 'great', 'buddha', 'walk', 'today', 'rudely', 'told', 'nothing', 'wrong', 'sermon', 'stepped', 'outside', 'found', 'car', 'stolen', 'edit', 'thanks', 'upvotes', 'inadvertently', 'upset', 'anyone', 'apologize', 'meant', 'joke', 'intended', 'give', 'u', 'chuckle', 'went', 'mixed', 'religion', 'seminar']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "['attire', 'edit', 'aware', 'posted', 'recently', 'care', 'research', 'either', 'deal', 'difference', 'well', 'dressed', 'man', 'unicycle', 'poorly', 'dressed', 'man', 'bicycle']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "['fell', 'forward', 'still', 'boat', 'scuba', 'diver', 'fall', 'backwards', 'boat']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "['found', 'website', 'guaranteed', 'real', 'virgin', 'nsfw']\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# print random 5 good jokes\n",
    "print('#' * 100)\n",
    "print('5 random good jokes:')\n",
    "print('#' * 100)\n",
    "for i in range(5):\n",
    "    print(df_good['tokens'][np.random.randint(0, num_good - 100)])\n",
    "    print('-' * 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the data\n",
    "#df_good.to_csv('./data/tok_good_jokes.csv', index=False)\n",
    "#df_bad.to_csv('./data/tok_bad_jokes.csv', index=False)"
   ]
  }
 ],
 "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}