{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling — CSV Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In these lessons, we're learning about a text analysis method called *topic modeling*. This method will help us identify the main topics or discourses within a collection of texts a single text that has been separated into smaller text chunks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Am I the Asshole?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<blockquote class=\"epigraph\" style=\" padding: 10px\">\n",
    "\n",
    "AITA for lying about my biggest fear on a quiz show and subsequently winning a car and making other contestants lose?\n",
    "\n",
    "\n",
    "<p class =\"attribution\">\n",
    "    —Reddit user iwonacar, <a href=\"https://www.reddit.com/r/AmItheAsshole/comments/dmfsum/aita_for_lying_about_my_biggest_fear_on_a_quiz/\">r/AmItheAsshole</a>\n",
    "    </p>\n",
    "    \n",
    "</blockquote>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this particular lesson, we're going to use [Little MALLET Wrapper](https://github.com/maria-antoniak/little-mallet-wrapper), a Python wrapper for [MALLET](http://mallet.cs.umass.edu/topics.php), to topic model a CSV file with 2,932 Reddit posts from the subreddit [r/AmITheAsshole](https://www.reddit.com/r/AmItheAsshole/) that have at least an upvote score of 2,000. This is an online forum where people share their personal conflicts and ask the community to judge who's the a**hole in the story. This data was collected with PSAW, a wrapper for the Pushshift API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"attention\" name=\"html-admonition\" style=\"background: lightyellow;padding: 10px\">  \n",
    "<p class=\"title\">Attention</p>  \n",
    "    \n",
    "<p>If you're working in this Jupyter notebook on your own computer, you'll need to have both the Java Development Kit and MALLET pre-installed. For set up instructions, please see <a href=\"http://melaniewalsh.github.io/Intro-Cultural-Analytics/Text-Analysis/Topic-Modeling-Set-Up.html\">the previous lesson<a/>.  </p>\n",
    "    \n",
    "If you're working in this Jupyter notebook in the cloud via Binder, then the Java Development Kit and Mallet will already be installed. You're good to go!  \n",
    "     \n",
    "</div>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set MALLET Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Little MALLET Wrapper is a Python package built around MALLET, we first need to tell it where the bigger, Java-based MALLET lives.\n",
    "\n",
    "We're going to make a variable called `path_to_mallet` and assign it the file path of our MALLET program. We need to point it, specifically, to the \"mallet\" file inside the \"bin\" folder inside the \"mallet-2.0.8\" folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_mallet = 'mallet-2.0.8/bin/mallet'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If MALLET is located in another directory, then set your `path_to_mallet` to that file path."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install little_mallet_wrapper\n",
    "#!pip install seaborn\n",
    "#To install the most updated version of little_mallet_wrapper:\n",
    "#!!pip install git+https://github.com/maria-antoniak/little-mallet-wrapper.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's `import` the `little_mallet_wrapper` and the data viz library `seaborn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import little_mallet_wrapper\n",
    "import seaborn\n",
    "import glob\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import random\n",
    "pd.options.display.max_colwidth = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're also going to import [`glob`](https://docs.python.org/3/library/glob.html) and [`pathlib`](https://docs.python.org/3/library/pathlib.html#basic-use) for working with files and the file system, as well as pandas for working with the CSV data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Training Data From CSV File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"note\" name=\"html-admonition\" style=\"background: lightblue; padding: 10px\">\n",
    "<p class=\"title\">Note</p>   \n",
    "We're calling these text files our *training data*, because we're *training* our topic model with these texts. The topic model will be learning and extracting topics based on these texts.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"pandasreview\" name=\"html-admonition\" style=\"background: black; color: white; padding: 10px\">\n",
    "<p class=\"title\">Pandas</p>\n",
    " Do you need a refresher or introduction to the Python data analysis library Pandas? Be sure to check out <a href=\"https://melaniewalsh.github.io/Intro-Cultural-Analytics/Data-Analysis/Pandas-Basics-Part1.html\"> Pandas Basics (1-3) </a> in this textbook!\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_df = pd.read_csv(\"../data/top-reddit-aita-posts.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>author</th>\n",
       "      <th>full_date</th>\n",
       "      <th>date</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "      <th>url</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>upvote_score</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>num_crossposts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Additional-Pizza-805</td>\n",
       "      <td>2020-07-24 19:13:49+00:00</td>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>AITA for kicking my cousin off of my sister’s wedding Zoom call?</td>\n",
       "      <td>My [27M] older sister [30F] and her fiancé [31M] were planning for over a year for their wedding...</td>\n",
       "      <td>https://www.reddit.com/r/AmItheAsshole/comments/hx80wd/aita_for_kicking_my_cousin_off_of_my_sist...</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>11159</td>\n",
       "      <td>2209</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>decadel8ter</td>\n",
       "      <td>2020-07-24 14:37:13+00:00</td>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>AITA for resenting my family for something that happened over a decade ago?</td>\n",
       "      <td>when i was 15 i was in a car accident. i was riding my bike on new bike lanes that my city had i...</td>\n",
       "      <td>https://www.reddit.com/r/AmItheAsshole/comments/hx2vvl/aita_for_resenting_my_family_for_somethin...</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>2541</td>\n",
       "      <td>1143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Snoo_66130</td>\n",
       "      <td>2020-07-24 12:35:35+00:00</td>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>AITA for telling my step dad to stop trying to be my dad?</td>\n",
       "      <td>I'm   35, and my mom who is 52 is dating a man who is is 27. This is fucking   weird as hell and...</td>\n",
       "      <td>https://www.reddit.com/r/AmItheAsshole/comments/hx0zk7/aita_for_telling_my_step_dad_to_stop_tryi...</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>2809</td>\n",
       "      <td>1253</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ohnoihaveabluechair</td>\n",
       "      <td>2020-07-24 10:56:56+00:00</td>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>AITA for confronting my SIL for wearing clothes that belonged to me?</td>\n",
       "      <td>Some info: A few years ago, my family didn’t have a lot of spare money to buy a lot of things (l...</td>\n",
       "      <td>https://www.reddit.com/r/AmItheAsshole/comments/hwzpbu/aita_for_confronting_my_sil_for_wearing_c...</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>7581</td>\n",
       "      <td>1550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FormalLettuce3</td>\n",
       "      <td>2020-07-24 10:52:08+00:00</td>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>AITA for saying we'd only help with my ex's kid's party if we could tell people we're engaged?</td>\n",
       "      <td>This guy, \"Jack\", and I were together for about a year, and within a couple weeks of ending it I...</td>\n",
       "      <td>https://www.reddit.com/r/AmItheAsshole/comments/hwzncq/aita_for_saying_wed_only_help_with_my_exs...</td>\n",
       "      <td>AmItheAsshole</td>\n",
       "      <td>2915</td>\n",
       "      <td>1214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 author                  full_date        date  \\\n",
       "0  Additional-Pizza-805  2020-07-24 19:13:49+00:00  2020-07-24   \n",
       "1           decadel8ter  2020-07-24 14:37:13+00:00  2020-07-24   \n",
       "2            Snoo_66130  2020-07-24 12:35:35+00:00  2020-07-24   \n",
       "3   ohnoihaveabluechair  2020-07-24 10:56:56+00:00  2020-07-24   \n",
       "4        FormalLettuce3  2020-07-24 10:52:08+00:00  2020-07-24   \n",
       "\n",
       "                                                                                            title  \\\n",
       "0                                AITA for kicking my cousin off of my sister’s wedding Zoom call?   \n",
       "1                     AITA for resenting my family for something that happened over a decade ago?   \n",
       "2                                       AITA for telling my step dad to stop trying to be my dad?   \n",
       "3                            AITA for confronting my SIL for wearing clothes that belonged to me?   \n",
       "4  AITA for saying we'd only help with my ex's kid's party if we could tell people we're engaged?   \n",
       "\n",
       "                                                                                              selftext  \\\n",
       "0  My [27M] older sister [30F] and her fiancé [31M] were planning for over a year for their wedding...   \n",
       "1  when i was 15 i was in a car accident. i was riding my bike on new bike lanes that my city had i...   \n",
       "2  I'm   35, and my mom who is 52 is dating a man who is is 27. This is fucking   weird as hell and...   \n",
       "3  Some info: A few years ago, my family didn’t have a lot of spare money to buy a lot of things (l...   \n",
       "4  This guy, \"Jack\", and I were together for about a year, and within a couple weeks of ending it I...   \n",
       "\n",
       "                                                                                                   url  \\\n",
       "0  https://www.reddit.com/r/AmItheAsshole/comments/hx80wd/aita_for_kicking_my_cousin_off_of_my_sist...   \n",
       "1  https://www.reddit.com/r/AmItheAsshole/comments/hx2vvl/aita_for_resenting_my_family_for_somethin...   \n",
       "2  https://www.reddit.com/r/AmItheAsshole/comments/hx0zk7/aita_for_telling_my_step_dad_to_stop_tryi...   \n",
       "3  https://www.reddit.com/r/AmItheAsshole/comments/hwzpbu/aita_for_confronting_my_sil_for_wearing_c...   \n",
       "4  https://www.reddit.com/r/AmItheAsshole/comments/hwzncq/aita_for_saying_wed_only_help_with_my_exs...   \n",
       "\n",
       "       subreddit  upvote_score  num_comments  num_crossposts  \n",
       "0  AmItheAsshole         11159          2209               4  \n",
       "1  AmItheAsshole          2541          1143               0  \n",
       "2  AmItheAsshole          2809          1253               1  \n",
       "3  AmItheAsshole          7581          1550               1  \n",
       "4  AmItheAsshole          2915          1214               0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reddit_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_df['selftext'] = reddit_df['selftext'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process Reddit Posts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`little_mallet_wrapper.process_string(text, numbers='remove')`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we're going to process our texts with the function `little_mallet_wrapper.process_string()`. This function will take every individual post, transform all the text to lowercase as well as remove stopwords, punctuation, and numbers, and then add the processed text to our master list `training_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = [little_mallet_wrapper.process_string(text, numbers='remove') for text in reddit_df['selftext']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_texts = [text for text in reddit_df['selftext']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process Reddit Post Titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're also going to extract the file name for each Reddit post."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "reddit_titles = [title for title in reddit_df['title']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Dataset Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get training data summary statisitcs by using the funciton `little_mallet_wrapper.print_dataset_stats()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Documents: 2932\n",
      "Mean Number of Words per Document: 147.3\n",
      "Vocabulary Size: 19331\n"
     ]
    }
   ],
   "source": [
    "little_mallet_wrapper.print_dataset_stats(training_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Topic Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Number of Topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to make a variable `num_topics` and assign it the number of topics we want returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_topics = 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We already made a variable called `training_data`, which includes all of our processed Reddit post texts, so we can just set it equal to itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = training_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Other MALLET File Paths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we're going to set a file path where we want all our MALLET topic modeling data to be dumped. I'm going to output everything onto my Desktop inside a folder called \"topic-model-output\" and a subfolder specific to the Reddit posts called \"Reddit.\"\n",
    "\n",
    "All the other necessary variables below `output_directory_path` will be automatically created inside this directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change to your desired output directory\n",
    "output_directory_path = 'topic-model-output/reddit'\n",
    "\n",
    "#No need to change anything below here\n",
    "Path(f\"{output_directory_path}\").mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "path_to_training_data           = f\"{output_directory_path}/training.txt\"\n",
    "path_to_formatted_training_data = f\"{output_directory_path}/mallet.training\"\n",
    "path_to_model                   = f\"{output_directory_path}/mallet.model.{str(num_topics)}\"\n",
    "path_to_topic_keys              = f\"{output_directory_path}/mallet.topic_keys.{str(num_topics)}\"\n",
    "path_to_topic_distributions     = f\"{output_directory_path}/mallet.topic_distributions.{str(num_topics)}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Topic Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we're going to train our topic model with `little_mallet_wrapper.quick_train_topic_model()`. The topic model should take about 1-1.5 minutes to train and complete. If you want it, you can look at your Terminal or PowerShell and see what the model looks like as it's training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing data...\n",
      "Complete\n",
      "Training topic model...\n",
      "Complete\n"
     ]
    },
    {
     "data": {
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       "   0.19688897884208847,\n",
       "   0.0007300284446805396,\n",
       "   0.0021912511470316134,\n",
       "   0.0012669308697412253,\n",
       "   0.311717989727825,\n",
       "   0.03644772746374569],\n",
       "  [0.0007462052513234392,\n",
       "   0.0006155687096372843,\n",
       "   0.0014770242162644412,\n",
       "   0.21223446719124117,\n",
       "   0.18861685938272485,\n",
       "   0.000888160713458837,\n",
       "   0.05580707151094936,\n",
       "   0.16057528025053314,\n",
       "   0.012412214191834067,\n",
       "   0.22017469403325252,\n",
       "   0.0005852268504961548,\n",
       "   0.0017566151247497854,\n",
       "   0.13136032709770182,\n",
       "   0.0005334689934994158,\n",
       "   0.012216816482333734],\n",
       "  [0.00161649334898042,\n",
       "   0.0013334973496961148,\n",
       "   0.0031996556143767917,\n",
       "   0.11601317313204744,\n",
       "   0.0034672717046111273,\n",
       "   0.0019240093574597675,\n",
       "   0.20683082979662787,\n",
       "   0.04093470112618062,\n",
       "   0.002335023770758114,\n",
       "   0.3296409398851048,\n",
       "   0.0012677682310516828,\n",
       "   0.2861691005170172,\n",
       "   0.002200153595795113,\n",
       "   0.0011556459544470605,\n",
       "   0.0019117366158458418],\n",
       "  [0.0012253645477922296,\n",
       "   0.05684806127828719,\n",
       "   0.021037869062116157,\n",
       "   0.06933007094829713,\n",
       "   0.002628326201944101,\n",
       "   0.0014584735889811961,\n",
       "   0.03580513688203392,\n",
       "   0.18923554126763859,\n",
       "   0.0017700384283942886,\n",
       "   0.43600465803769556,\n",
       "   0.000961017406058512,\n",
       "   0.040109399343989535,\n",
       "   0.1412608482520122,\n",
       "   0.0008760243791118095,\n",
       "   0.0014491703756475908],\n",
       "  [0.020821924700280537,\n",
       "   0.00044073041000757305,\n",
       "   0.025402703568055407,\n",
       "   0.31831292016371043,\n",
       "   0.0011459580930769442,\n",
       "   0.008750963686880324,\n",
       "   0.2347178974375846,\n",
       "   0.1474277812351022,\n",
       "   0.0048292744719112485,\n",
       "   0.21038699836141783,\n",
       "   0.0004190064662620541,\n",
       "   0.025602883034298494,\n",
       "   0.0007271664968628483,\n",
       "   0.0003819492520499683,\n",
       "   0.0006318426224995765],\n",
       "  [0.0010171224827424186,\n",
       "   0.0008390570464820244,\n",
       "   0.0020132725349401117,\n",
       "   0.10389599126982152,\n",
       "   0.009906340203169994,\n",
       "   0.00893529579333834,\n",
       "   0.045169665339854025,\n",
       "   0.0025827051515788157,\n",
       "   0.11733942548788612,\n",
       "   0.3464593815879142,\n",
       "   0.0007976992738773531,\n",
       "   0.3577296290387727,\n",
       "   0.0013843704888617462,\n",
       "   0.0007271502125881397,\n",
       "   0.0012028940881724994],\n",
       "  [0.0021082597544981705,\n",
       "   0.0017391712727228455,\n",
       "   0.004173048509169224,\n",
       "   0.3594556423112671,\n",
       "   0.00452207823641992,\n",
       "   0.16262407489528447,\n",
       "   0.09362627331115789,\n",
       "   0.005353350674274337,\n",
       "   0.0030453800782978178,\n",
       "   0.20576300772602252,\n",
       "   0.0016534461717664481,\n",
       "   0.1490662514717793,\n",
       "   0.0028694799657882518,\n",
       "   0.0015072142782066437,\n",
       "   0.002493321343345123],\n",
       "  [0.19898136984151144,\n",
       "   0.0006942581586640128,\n",
       "   0.0016658353432066622,\n",
       "   0.009267043726895912,\n",
       "   0.15520370013983747,\n",
       "   0.0010016961743994086,\n",
       "   0.414479318939821,\n",
       "   0.03409502738584935,\n",
       "   0.02678210509132797,\n",
       "   0.08213834318996507,\n",
       "   0.013443248964319726,\n",
       "   0.008372772612765686,\n",
       "   0.00753707069627584,\n",
       "   0.0006016634623119188,\n",
       "   0.045736546272848524],\n",
       "  [0.006881337541844437,\n",
       "   0.0006604856481826538,\n",
       "   0.062391630400824814,\n",
       "   0.10610717270816354,\n",
       "   0.09292759673120427,\n",
       "   0.061759798590252706,\n",
       "   0.029475797673676158,\n",
       "   0.002033043752218531,\n",
       "   0.0011565449973354157,\n",
       "   0.3335313729912835,\n",
       "   0.0006279297983024605,\n",
       "   0.014046158240565569,\n",
       "   0.2868818461929399,\n",
       "   0.0005723952638275365,\n",
       "   0.0009468894693785477],\n",
       "  [0.0009510064990138774,\n",
       "   0.0007845158452267454,\n",
       "   0.08855303246621082,\n",
       "   0.18381307174174846,\n",
       "   0.06704281773917377,\n",
       "   0.001131922630094234,\n",
       "   0.2589100744434571,\n",
       "   0.03130503118509972,\n",
       "   0.0013737283749070063,\n",
       "   0.17948744457654567,\n",
       "   0.0007458464507348127,\n",
       "   0.08890935905405402,\n",
       "   0.0012943822934685322,\n",
       "   0.09457306430218927,\n",
       "   0.0011247023980760057],\n",
       "  [0.0008751561641214331,\n",
       "   0.0007219444646414595,\n",
       "   0.0017322671545501782,\n",
       "   0.23561748045213607,\n",
       "   0.0018771522981447242,\n",
       "   0.03427412463113462,\n",
       "   0.23161343964493702,\n",
       "   0.1019196659134993,\n",
       "   0.09431511175018571,\n",
       "   0.23163685646981447,\n",
       "   0.04721183383359051,\n",
       "   0.0020601738619330273,\n",
       "   0.014484137741661922,\n",
       "   0.0006256571864117162,\n",
       "   0.0010349984332378482],\n",
       "  [0.0013651082709869133,\n",
       "   0.19810874400143502,\n",
       "   0.002702068861743506,\n",
       "   0.1705442083982888,\n",
       "   0.0029280672789089683,\n",
       "   0.001624801666509275,\n",
       "   0.02952094030366572,\n",
       "   0.003466320156874962,\n",
       "   0.0019718981611792337,\n",
       "   0.3405826600138568,\n",
       "   0.0010706143016268376,\n",
       "   0.0032135526136859785,\n",
       "   0.0018580019973208604,\n",
       "   0.21869356361078332,\n",
       "   0.022349450363133856],\n",
       "  [0.1338431498235277,\n",
       "   0.012846554761838653,\n",
       "   0.030824621495110654,\n",
       "   0.17147740106794468,\n",
       "   0.03340276292083453,\n",
       "   0.018535388599407066,\n",
       "   0.10022797397542552,\n",
       "   0.03954303620063915,\n",
       "   0.022494990895988647,\n",
       "   0.21891494960507782,\n",
       "   0.01221333811367245,\n",
       "   0.03665951833203013,\n",
       "   0.021195687909900382,\n",
       "   0.011133182261280586,\n",
       "   0.13668744403732208],\n",
       "  ...])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "little_mallet_wrapper.quick_train_topic_model(path_to_mallet,\n",
    "                                             output_directory_path,\n",
    "                                             num_topics,\n",
    "                                             training_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the topic model finishes, it will output your results to your `output_directory_path`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Topics and Top Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To examine the 15 topics that the topic model extracted from the Reddit posts, run the cell below. This code uses the `little_mallet_wrapper.load_topic_keys()` function to read and process the MALLET topic model output from your computer, specifically the file \"mallet.topic_keys.15\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 0✨\n",
      "\n",
      "['baby', 'pregnant', 'child', 'husband', 'pregnancy', 'pain', 'birth', 'hospital', 'doctor', 'could', 'sil', 'think', 'abortion', 'would', 'medical', 'surgery', 'help', 'weeks', 'control', 'children']\n",
      "\n",
      "✨Topic 1✨\n",
      "\n",
      "['table', 'store', 'manager', 'drink', 'order', 'restaurant', 'bar', 'drinking', 'drinks', 'pay', 'tip', 'place', 'wine', 'people', 'coffee', 'bottle', 'food', 'alcohol', 'beer', 'ordered']\n",
      "\n",
      "✨Topic 2✨\n",
      "\n",
      "['money', 'pay', 'buy', 'would', 'paying', 'college', 'job', 'bought', 'house', 'gift', 'paid', 'car', 'give', 'afford', 'since', 'year', 'got', 'rent', 'working', 'much']\n",
      "\n",
      "✨Topic 3✨\n",
      "\n",
      "['time', 'get', 'like', 'want', 'work', 'one', 'doesn', 'would', 'going', 'take', 'even', 'home', 'really', 'think', 'day', 'every', 'make', 'feel', 'something', 'week']\n",
      "\n",
      "✨Topic 4✨\n",
      "\n",
      "['sister', 'mom', 'dad', 'family', 'parents', 'brother', 'mother', 'cousin', 'aunt', 'year', 'father', 'also', 'house', 'siblings', 'older', 'nephew', 'always', 'close', 'birthday', 'years']\n",
      "\n",
      "✨Topic 5✨\n",
      "\n",
      "['school', 'work', 'office', 'class', 'name', 'people', 'company', 'high', 'teacher', 'boss', 'job', 'coworkers', 'students', 'team', 'college', 'year', 'one', 'student', 'english', 'coworker']\n",
      "\n",
      "✨Topic 6✨\n",
      "\n",
      "['years', 'told', 'would', 'never', 'wife', 'life', 'family', 'ago', 'relationship', 'time', 'know', 'want', 'together', 'love', 'months', 'married', 'feel', 'even', 'things', 'much']\n",
      "\n",
      "✨Topic 7✨\n",
      "\n",
      "['kids', 'daughter', 'son', 'husband', 'wife', 'old', 'child', 'year', 'kid', 'name', 'mother', 'children', 'mom', 'two', 'told', 'school', 'think', 'house', 'baby', 'parents']\n",
      "\n",
      "✨Topic 8✨\n",
      "\n",
      "['room', 'house', 'dog', 'door', 'sleep', 'bed', 'bathroom', 'apartment', 'roommate', 'living', 'live', 'cat', 'use', 'stuff', 'home', 'dogs', 'girlfriend', 'bedroom', 'moved', 'place']\n",
      "\n",
      "✨Topic 9✨\n",
      "\n",
      "['said', 'told', 'didn', 'got', 'asked', 'like', 'really', 'went', 'back', 'one', 'friend', 'could', 'friends', 'get', 'know', 'started', 'wasn', 'would', 'left', 'aita']\n",
      "\n",
      "✨Topic 10✨\n",
      "\n",
      "['wedding', 'dress', 'hair', 'wear', 'ring', 'married', 'fiancé', 'party', 'wearing', 'one', 'would', 'think', 'tattoo', 'family', 'husband', 'getting', 'makeup', 'engaged', 'fiance', 'ceremony']\n",
      "\n",
      "✨Topic 11✨\n",
      "\n",
      "['people', 'like', 'girls', 'gay', 'think', 'girl', 'women', 'said', 'always', 'group', 'girlfriend', 'friends', 'woman', 'one', 'white', 'doesn', 'really', 'look', 'know', 'thing']\n",
      "\n",
      "✨Topic 12✨\n",
      "\n",
      "['car', 'seat', 'back', 'get', 'minutes', 'around', 'front', 'woman', 'guy', 'next', 'move', 'people', 'away', 'flight', 'door', 'phone', 'lady', 'sit', 'park', 'drive']\n",
      "\n",
      "✨Topic 13✨\n",
      "\n",
      "['food', 'eat', 'eating', 'vegan', 'dinner', 'make', 'meat', 'cook', 'weight', 'cooking', 'meal', 'ate', 'chicken', 'lunch', 'fridge', 'eats', 'diet', 'cake', 'family', 'hungry']\n",
      "\n",
      "✨Topic 14✨\n",
      "\n",
      "['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "topics = little_mallet_wrapper.load_topic_keys(path_to_topic_keys)\n",
    "\n",
    "for topic_number, topic in enumerate(topics):\n",
    "    print(f\"✨Topic {topic_number}✨\\n\\n{topic}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Topic Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MALLET also calculates the likely mixture of these topics for every single Reddit post in the corpus. This mixture is really a probability distribution, that is, the probability that each topic exists in the document. We can use these probability distributions to examine which of the above topics are strongly associated with which specific posts.\n",
    "\n",
    "To get the topic distributions, we're going to use the `little_mallet_wrapper.load_topic_distributions()` function, which will read and process the MALLET topic model output, specifically the file \"mallet.topic_distributions.15\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_distributions = little_mallet_wrapper.load_topic_distributions(path_to_topic_distributions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.011076196974682066,\n",
       " 0.000564392649472897,\n",
       " 0.0013542299952905628,\n",
       " 0.1530219806345308,\n",
       " 0.1521519084626053,\n",
       " 0.000814322382504041,\n",
       " 0.08234356736699737,\n",
       " 0.006933273680468054,\n",
       " 0.000988281118714402,\n",
       " 0.33177055578329395,\n",
       " 0.2551412694103212,\n",
       " 0.0016105767704565042,\n",
       " 0.0009311983390601434,\n",
       " 0.0004891183939972939,\n",
       " 0.000809128037605374]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_distributions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a bit easier to understand if we pair these probabilities with the topics themselves. As you can see below, Topic 8 \"'room', 'house', 'dog', 'door', 'sleep', 'bed'\" has a relatively high probability of existing in the Reddit post \"AITA for walking my cat on a trail frequented by dogs?\" while Topic 10 \"'wedding', 'dress', 'hair', 'wear', 'ring', 'married'\" has a relatively low probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic Distributions for AITA for walking my cat on a trail frequented by dogs?\n",
      "\n",
      "✨Topic 0 ['baby', 'pregnant', 'child', 'husband', 'pregnancy', 'pain'] ✨\n",
      "Probability: 0.002\n",
      "\n",
      "✨Topic 1 ['table', 'store', 'manager', 'drink', 'order', 'restaurant'] ✨\n",
      "Probability: 0.002\n",
      "\n",
      "✨Topic 2 ['money', 'pay', 'buy', 'would', 'paying', 'college'] ✨\n",
      "Probability: 0.024\n",
      "\n",
      "✨Topic 3 ['time', 'get', 'like', 'want', 'work', 'one'] ✨\n",
      "Probability: 0.177\n",
      "\n",
      "✨Topic 4 ['sister', 'mom', 'dad', 'family', 'parents', 'brother'] ✨\n",
      "Probability: 0.024\n",
      "\n",
      "✨Topic 5 ['school', 'work', 'office', 'class', 'name', 'people'] ✨\n",
      "Probability: 0.003\n",
      "\n",
      "✨Topic 6 ['years', 'told', 'would', 'never', 'wife', 'life'] ✨\n",
      "Probability: 0.035\n",
      "\n",
      "✨Topic 7 ['kids', 'daughter', 'son', 'husband', 'wife', 'old'] ✨\n",
      "Probability: 0.006\n",
      "\n",
      "✨Topic 8 ['room', 'house', 'dog', 'door', 'sleep', 'bed'] ✨\n",
      "Probability: 0.509\n",
      "\n",
      "✨Topic 9 ['said', 'told', 'didn', 'got', 'asked', 'like'] ✨\n",
      "Probability: 0.091\n",
      "\n",
      "✨Topic 10 ['wedding', 'dress', 'hair', 'wear', 'ring', 'married'] ✨\n",
      "Probability: 0.002\n",
      "\n",
      "✨Topic 11 ['people', 'like', 'girls', 'gay', 'think', 'girl'] ✨\n",
      "Probability: 0.006\n",
      "\n",
      "✨Topic 12 ['car', 'seat', 'back', 'get', 'minutes', 'around'] ✨\n",
      "Probability: 0.022\n",
      "\n",
      "✨Topic 13 ['food', 'eat', 'eating', 'vegan', 'dinner', 'make'] ✨\n",
      "Probability: 0.02\n",
      "\n",
      "✨Topic 14 ['phone', 'facebook', 'post', 'pictures', 'social', 'account'] ✨\n",
      "Probability: 0.078\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reddit_post_to_check = \"AITA for walking my cat on a trail frequented by dogs?\"\n",
    "\n",
    "reddit_post_number = reddit_titles.index(reddit_post_to_check)\n",
    "\n",
    "print(f\"Topic Distributions for {reddit_titles[reddit_post_number]}\\n\")\n",
    "for topic_number, (topic, topic_distribution) in enumerate(zip(topics, topic_distributions[reddit_post_number])):\n",
    "    print(f\"✨Topic {topic_number} {topic[:6]} ✨\\nProbability: {round(topic_distribution, 3)}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Heatmap of Topics and Texts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize and compare these topic probability distributions with a heatmap by using the `little_mallet_wrapper.plot_categories_by_topics_heatmap()` function.\n",
    "\n",
    "We have everything we need for the heatmap except for our list of `target_labels`, the sample of texts that we'd like to visualize and compare with the heatmap. Below we make our list of desired target labels by randomly selecting 6 Reddit post titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_labels = random.sample(reddit_titles, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "little_mallet_wrapper.plot_categories_by_topics_heatmap(reddit_titles,\n",
    "                                      topic_distributions,\n",
    "                                      topics, \n",
    "                                      output_directory_path + '/categories_by_topics.pdf',\n",
    "                                      target_labels=target_labels,\n",
    "                                      dim= (18, 8)\n",
    "                                     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `plot_categories_by_topics_heatmap()` function also helpfully outputs a PDF of the heatmap to `output_directory_path + '/categories_by_topics.pdf'`. We can download this PDF and explore it in more detail or embed it in an article or blog post!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"600\"\n",
       "            src=\"topic-model-output/reddit/categories_by_topics.pdf\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fc0d85fec40>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame(\"topic-model-output/reddit/categories_by_topics.pdf\", width=1000, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Top Titles Per Topic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also display the Reddit posts and titles that have the highest probability for every topic with the `little_mallet_wrapper.get_top_docs()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data_reddit_titles = dict(zip(training_data, reddit_titles))\n",
    "training_data_original_text = dict(zip(training_data, original_texts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll make our own function `display_top_titles_per_topic()` that will display the top Reddit post titles for every topic. This function accepts a given `topic_number` as well as a desired `number_of_documents` to display."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_top_titles_per_topic(topic_number=0, number_of_documents=5):\n",
    "    \n",
    "    print(f\"✨Topic {topic_number}✨\\n\\n{topics[topic_number]}\\n\")\n",
    "\n",
    "    for probability, document in little_mallet_wrapper.get_top_docs(training_data, topic_distributions, topic_number, n=number_of_documents):\n",
    "        print(round(probability, 4), training_data_reddit_titles[document] + \"\\n\")\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topic 0**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the top 5 Reddit post titles with the highest probability of containing Topic 0, we will run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 0✨\n",
      "\n",
      "['baby', 'pregnant', 'child', 'husband', 'pregnancy', 'pain', 'birth', 'hospital', 'doctor', 'could', 'sil', 'think', 'abortion', 'would', 'medical', 'surgery', 'help', 'weeks', 'control', 'children']\n",
      "\n",
      "0.4107 AITA for offering my DNA because my brother won't do a paternity test\n",
      "\n",
      "0.378 AITA for causing a scene in the hospital over an IV?\n",
      "\n",
      "0.3728 AITA for lying about my birth control use?\n",
      "\n",
      "0.363 AITA for asking my immediate family to get the flu shot if they want to see our newborn in the hospital\n",
      "\n",
      "0.3542 AITA for feeling jealous that my sister is getting more attention for her illness than I am for mine?\n",
      "\n"
     ]
    }
   ],
   "source": [
    "display_top_titles_per_topic(topic_number=0, number_of_documents=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What would you label this topic?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the top 5 Reddit post titles with the highest probability of containing Topic 9, we will run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 10✨\n",
      "\n",
      "['wedding', 'dress', 'hair', 'wear', 'ring', 'married', 'fiancé', 'party', 'wearing', 'one', 'would', 'think', 'tattoo', 'family', 'husband', 'getting', 'makeup', 'engaged', 'fiance', 'ceremony']\n",
      "\n",
      "0.4522 AITA for showing up at my cousin's wedding in a hoodie and jean shorts?\n",
      "\n",
      "0.438 AITA? I wore an accessory ring to work knowing that my co-worker has the same ring for her engagement ring.\n",
      "\n",
      "0.4347 AITA for not including a bridesmaid in photographs because she wore a suit?\n",
      "\n",
      "0.4049 AITA for dressing nicer than the couple getting married?\n",
      "\n",
      "0.3982 AITA for getting a tattoo before my brother's wedding?\n",
      "\n"
     ]
    }
   ],
   "source": [
    "display_top_titles_per_topic(topic_number=10, number_of_documents=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What would you label this topic?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the top 7 Reddit posts with the highest probability of containing Topic 8, we will run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 8✨\n",
      "\n",
      "['room', 'house', 'dog', 'door', 'sleep', 'bed', 'bathroom', 'apartment', 'roommate', 'living', 'live', 'cat', 'use', 'stuff', 'home', 'dogs', 'girlfriend', 'bedroom', 'moved', 'place']\n",
      "\n",
      "0.5773 AITA for refusing to eat my GF cooking because her cats litter is disgusting?\n",
      "\n",
      "0.5279 AITA for peeing in places that are not the toilet?\n",
      "\n",
      "0.5087 AITA for walking my cat on a trail frequented by dogs?\n",
      "\n",
      "0.5039 Aita for not wanting all 3 of my triplet children to have to share a room\n",
      "\n",
      "0.4808 WIBTA if I told my roommate his girlfriend is getting too comfortable in our home?\n",
      "\n",
      "0.4544 AITA for turning off the dryer on someone who removed my laundry before it was done?\n",
      "\n",
      "0.4374 AITA for not allowing my roommate's girlfriend to use my bathroom causing her to piss herself\n",
      "\n"
     ]
    }
   ],
   "source": [
    "display_top_titles_per_topic(topic_number=8, number_of_documents=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What would you label this topic?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Topic Words in Context of Original Text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often it's useful to actually look at the document that has ranked highly for a given topic and puzzle out why it ranks so highly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the original Reddit post texts that rank highly for a given topic, with the relevant topic words **bolded** for emphasis, we are going to make the function `display_bolded_topic_words_in_context()`.\n",
    "\n",
    "In the cell below, we're importing two special Jupyter notebook display modules, which will allow us to make the relevant topic words **bolded**, as well as the regular expressions library `re`, which will allow us to find and replace the correct words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import Markdown, display\n",
    "import re\n",
    "\n",
    "def display_bolded_topic_words_in_context(topic_number=3, number_of_documents=3, custom_words=None):\n",
    "\n",
    "    for probability, document in little_mallet_wrapper.get_top_docs(training_data, topic_distributions, topic_number, n=number_of_documents):\n",
    "        \n",
    "        print(f\"✨Topic {topic_number}✨\\n\\n{topics[topic_number]}\\n\")\n",
    "        \n",
    "        probability = f\"✨✨✨\\n\\n**{probability}**\"\n",
    "        reddit_title = f\"**{training_data_reddit_titles[document]}**\"\n",
    "        original_text = training_data_original_text[document]\n",
    "        topic_words = topics[topic_number]\n",
    "        topic_words = custom_words if custom_words != None else topic_words\n",
    "\n",
    "        for word in topic_words:\n",
    "            if word in original_text:\n",
    "                original_text = re.sub(f\"\\\\b{word}\\\\b\", f\"**{word}**\", original_text)\n",
    "\n",
    "        display(Markdown(probability)), display(Markdown(reddit_title)), display(Markdown(original_text))\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topic 3**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the top 3 original Reddit posts with the highest probability of containing Topic 0 and with relevant topic words bolded, we will run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 3✨\n",
      "\n",
      "['time', 'get', 'like', 'want', 'work', 'one', 'doesn', 'would', 'going', 'take', 'even', 'home', 'really', 'think', 'day', 'every', 'make', 'feel', 'something', 'week']\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "✨✨✨\n",
       "\n",
       "**0.7187352724737381**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AITA for setting my boyfriend up to fail?**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "My boyfriend has always been extremely shitty about getting out of bed in the morning. If we have fights, it’s usually stemming from his behavior and general inability to wake himself up. When we first met in college he **would** sleep until 6pm or later **every** single **day** unless someone woke him up.\n",
       "\n",
       "So now we live together and for about two years, I’ve woken up early to **get** him ready for **work**. He refuses to set his own alarms (we’ve tried but he sleeps through them), meaning I have to set several and try to wake him up several more times a morning. When he finally does **get** up, he’s usually a complete and total dick. I mean just nasty to me. He usually ends up with about 30 minutes before **work** to **get** ready, but likes to **take** his **time** and watch the news, meaning he’s usually a little late.\n",
       "\n",
       "Here’s where I’m concerned I’m an asshole: Recently he’s started taking an additional 20 minutes to smoke weed while watching the news **every** morning. (Idc about the weed, he works at a pizza shop) But he refuses to **get** up any earlier, making him around 45 minutes late to **work** **every** **day**. All of this stresses me out *so badly*. I **get** stressed because I have to do all this shit to **get** him out of bed in plenty of **time** and then he’s still willfully 45 minutes late everyday. Not to mention the added stress of him acting **like** I am doing this just to hurt him. So, I’ve decided to stop waking him up. No more alarms, no more begging, I’m just **going** to let him **get** up when he decides is right. \n",
       "\n",
       "He will definitely sleep until late afternoon unless I intervene. He will definitely lose his job. I **feel** the need to prevent that. At the same **time**, I’m so done being screamed at **every** morning. WIBTA if I set him up to fail **like** this?"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 3✨\n",
      "\n",
      "['time', 'get', 'like', 'want', 'work', 'one', 'doesn', 'would', 'going', 'take', 'even', 'home', 'really', 'think', 'day', 'every', 'make', 'feel', 'something', 'week']\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "✨✨✨\n",
       "\n",
       "**0.674436146664204**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AITA for complaining she is constantly making plans for both of us?**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "I (27M) am a “chill at **home**” type of person.  I was diagnosed with depression and its largest effect is on my energy levels.  My weight isn’t doing me any favors either.  My wife (27F) isn’t incredibly active, but can’t go an entire **day** without making plans to do **something**, which is fine.  I **like** doing things with my wife and I can deal deal with her plans most of the **time**, but sometimes I **get** **home** from **work** and just need to sit in my lazyboy and watch a Netflix. \n",
       "\n",
       "For example, It’s common to go from having an entire weekend with no plans to her sister and 5 year old to come visit for a whole **day**. Then the next **day** we are **going** to her grandparents. The whole weekend gone in two sentences. I know it might seem normal but it just goes on and on and on. I can’t remember the last **time** i had a no-obligation, **get** stuff done (or not) kind of **day**.  \n",
       "\n",
       "There are two more layers to this. \n",
       "\n",
       "1.  She NEVER asks me about any of the plans. She is happy to commit my **time** and then tell me the night before.  If I ever **want** to **make** my own plans I have to do so around her plans. Plus I always talk to her about it before making them.\n",
       "\n",
       "2.  I don’t know if she does it on purpose or not, but she makes me **feel** bad telling her I don’t **want** to do what she has planned. Sometimes i **think** it is on purpose because she is always saying “you never **want** to do anything”. I could do 8 or 9 of 10 things she wants and still be told this.  It just seems manipulative and inconsiderate. \n",
       "\n",
       "This has led to me becoming bitter at times.  We have small arguments about it from **time**-to-**time**, she keeps making plans, and I keep piling on stress. It’s gotten to the point that I contest her plans **every** **time**.\n",
       "\n",
       "However, today she laid out “our” weekend plans and i said “Nope, I’m staying **home**. You can do that on your own for once.” She comes back with “We haven’t seen them in forever.” I said “Well I could go a little longer.” She stormed off and I left for **work**. \n",
       "\n",
       "From her perspective I’m sure it’s annoying to have to force me out the door.  I could see her thinking that I’m just being lazy. We **get** along great but she just **doesn**’t understand that I need more **time** to relax than normal. AITA for being complaining often and saying what I did?"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 3✨\n",
      "\n",
      "['time', 'get', 'like', 'want', 'work', 'one', 'doesn', 'would', 'going', 'take', 'even', 'home', 'really', 'think', 'day', 'every', 'make', 'feel', 'something', 'week']\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "✨✨✨\n",
       "\n",
       "**0.6179777159182313**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AITA for reporting a pregnant coworker to management after discovering we’re all covering for her?**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "I am 5 months pregnant, coworker is 4 months pregnant, we **work** in a small team of 8 and often cover each other shifts, stay back when someone has appointments and generally help each other out. \n",
       "I have been **really** struggling with my pregnancy and have been careful to schedule appointments on my days off and to **take** as many precautions as I can to avoid impacting everyone else. \n",
       "Coworker, who is saying that she’s having a wonderful, enjoyable pregnancy, is showing up late, then **going** for a smoke and a coffee before starting. “I’m just running late, at OBGYN” quickly becomes and hour or more late, and isn’t being noted on her **time** sheets, so the person covering her isn’t getting paid for that **time** and it’s especially difficult for me when I’m already struggling physically to not know what **time** I’ll be able to go **home**. \n",
       "I recently learned that other coworkers have been covering a significant part of her tasks, because they **feel** badly for her having to do it whilst pregnant- tasks that I’m still doing and have been doing with ease. \n",
       "\n",
       "Our manager is very accommodating and has made several offers to purchase alternative equipment and **make** changes in the workplace, that she has refused (as have I so far) because it’s easy to complete these tasks the way things are now. I know they are not aware that coworker simply isn’t doing those things, because our manager has asked me about it recently. \n",
       "\n",
       "WIBTA if I discussed this with management? Other staff are complaining to me about the additional workload. I’m also being expected to put in additional effort at a **time** that I have no extra energy to give. Trying to bring it up with coworker hasn’t made a difference, and other staff have already told me they’re too tired to help me when I’m struggling because they’re already helping her. \n",
       "I’m worried that I’m just being petty about everyone fussing over her, when I’ve been working so hard to not impact the rest of the workplace."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_bolded_topic_words_in_context(topic_number=3, number_of_documents=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topic 8**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display the top 3 original Reddit posts with the highest probability of containing Topic 8 and with relevant topic words bolded, we will run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true,
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 14✨\n",
      "\n",
      "['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "✨✨✨\n",
       "\n",
       "**0.44218633047423606**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AITA for suing over not winning a contest?**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "I entered a contest from a motion capture company who makes mocap suits. Since I am making a **video** **game** with their suit already, I figured i'd enter and try to win the cash prize + a brand new upgraded suit.\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "Only 8 people entered the contest total, all of which were publicly **posted** with videos. Anyone could go on the site and click Vote and ultimately, after 30 days, the winners would be chosen\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "There were 5 prizes:\n",
       "\n",
       "1. $3000 + mocap suit\n",
       "2. $2000 + mocap suit\n",
       "3. $1000 + mocap suit\n",
       "4. mocap suit\n",
       "5. mocap suit\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "After the contest started, about four days in, they allowed an additional four people to enter. I complained about it that it wasn't fair, everyone had three months to prepare their videos and **post** them. They told me too bad.\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "I have a fair sized community behind my **game**, so they all voted and we obviously stayed at #1 throughout the whole thing... Until the others figured out how click bots worked. I took **video** of two people using click bots to get 700 votes in minutes. I **sent** them over and asked them to place a captcha and a cookie drop on the voting mechanism to keep it fair. They declined. I asked to be removed from the contest, so their PR manager instead reached out to me and apologized and took my advice and implemented those methods. The cheaters votes stopped.\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "I continued promoting the contest on **social** platforms until the end. All in all, just to reach my own FB followers, my ads cost me $400 to run.\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "I finished the contest with several thousand votes, winners were to be announced on July 15th (today). When they were announced, I was nowhere on there. Not even for honorable mention.\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "Instead someone with 7 votes got 1st place. Someone with 14 votes got 2nd, and so on.\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "My community erupted on their **facebook** page wondering why we won nothing, the company said it was because \"cheating was evident in some of the votes, so they made an administrative decision to give prizes instead to people of their choosing\".\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "I messaged them and asked them for an explanation and they old me \"because you complained throughout the contest\" and \"we do not feel as though you are pitching a product at all\"\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "Meanwhile, for the last year and a half, they have been promoting my **game** on their **social** channels and have it listed on their highlighted products section on their website. \n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "I gave them an opportunity to fix it, but they said they stand by their decision. \n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "So, I am suing them for the prize I believe I am owed. \n",
       "\n",
       "  \n",
       "AITA?"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 14✨\n",
      "\n",
      "['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "✨✨✨\n",
       "\n",
       "**0.41619810812974495**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AITA for deleting a video off my friends phone.**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "so my friend runs a Instagram **account** with around 20,000 followers. she loves to update her fans with what she is doing and so sometimes when we are out she will be on her **phone** taking videos and such. \n",
       "\n",
       "yesterday we all decided to have a girls movie night and hang out in pjs and watch scary movies. now my pjs are a crop with no bra and pj shorts. \n",
       "I thought that because I was around friends it would be ok. \n",
       "\n",
       "I then noticed that she was updating her Instagram story and I saw that she was showing all of us in the **video**. me and another girl asked her to delete the **video** as we where both in reveling clothes and did not want that **posted** on **social** **media**. she refused so i grabbed her **phone** off her and deleted the **video**. \n",
       "\n",
       "she got pissed and left, and told me that I have no right to take her **phone** and delete her **video**. \n",
       "I'm now thinking I overstepped. \n",
       "\n",
       "AITA?"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✨Topic 14✨\n",
      "\n",
      "['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "✨✨✨\n",
       "\n",
       "**0.39980537706581076**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**AITA for messing with my sister's fake profile?**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Going to try to make this quick. I'm 22 and my sister is 18. I went to my sister's room to grab a top she had borrowed(we do this all the time, borrow clothes go into each other's rooms to get them back) and I notice she has Facebook open and the profile pic is...well..me. I have to do a double take because it really caught me off guard. I know I shouldn't have but I snooped at the profile and it was just **pictures** of myself- my sister literally has a fake Facebook profile of me. \n",
       "\n",
       "I'm livid at this point for many reasons. 1. I don't do **social** **media**. I'm a private person and I don't like **pictures** of me to be on profiles like that. 2. Some of the **pictures** she had were ones that she took without me knowing 3. Other **pictures** that she had were ones I had **sent** to friends which means she has gone onto my computer and taken **pictures** off. \n",
       "\n",
       "I delete all of the **pictures** and I check her computer and find a folder filled with **pictures** that she has taken of me and ones that were on my computer- not even all of me per se, ones of my friends, group **photos**, food I had taken **pictures** of. I go through even more and find out that she has even stolen nudes that I had taken for my boyfriend.\n",
       "\n",
       "I deleted the folder completely and went back to the fake **facebook** profile. I uploaded a single **picture** of her with the caption \"The real me\", changed her name(she was using my middle name and a fake last name) and then left her room. I've heard her sobbing from her room all day but I'm too angry and embarrassed to even confront her right now. AITA for messing with the profile and not just confronting her in the beginning?"
      ],
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     "text": [
      "✨Topic 14✨\n",
      "\n",
      "['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']\n",
      "\n"
     ]
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    {
     "data": {
      "text/markdown": [
       "✨✨✨\n",
       "\n",
       "**0.39082206926846264**"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/markdown": [
       "**[Update} WIBTAA if I take only one dog for a walk.**"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
     },
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     "data": {
      "text/markdown": [
       "Original **post** \n",
       "\n",
       "[https://www.reddit.com/r/AmItheAsshole/comments/choiv4/wibta\\_if\\_i\\_started\\_taking\\_only\\_one\\_of\\_my\\_dogs\\_for/](https://www.reddit.com/r/AmItheAsshole/comments/choiv4/wibta_if_i_started_taking_only_one_of_my_dogs_for/)\n",
       "\n",
       "Update. Baxter couldn't do stairs or lay down without hurting.. even when I gave him drugs.\n",
       "\n",
       "&amp;#x200B;\n",
       "\n",
       "I let him go this morning. He is with Lincoln, Indigo and Winston now. I have never cried so much."
      ],
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     "output_type": "stream",
     "text": [
      "✨Topic 14✨\n",
      "\n",
      "['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']\n",
      "\n"
     ]
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    {
     "data": {
      "text/markdown": [
       "✨✨✨\n",
       "\n",
       "**0.38733474788555244**"
      ],
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       "<IPython.core.display.Markdown object>"
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    {
     "data": {
      "text/markdown": [
       "**AITA Changing someone’s instagram password for using my email**"
      ],
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      "text/markdown": [
       "I got an email that “my” **account** name had been changed. Since I didn’t have an **account** under the supposed name I checked it out. \n",
       "\n",
       "It wasn’t my **account** so I dm’ed the other person, “Hi it’s actually [name]. Do you want to change your email?” To which he responded, “No I’m **also** actually [name] bitch.”\n",
       "\n",
       "Now this user appeared to be a kid so I decided to use this as a learning opportunity. I changed his password and deleted his recovery **phone** **number**. I then gave him a bio which said, “dm me @[my **account**] with your email so I can change it for you.”"
      ],
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      ]
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