Topic Modeling — CSV Files

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.


Dataset

Am I the Asshole?

AITA for lying about my biggest fear on a quiz show and subsequently winning a car and making other contestants lose?

—Reddit user iwonacar, r/AmItheAsshole

In this particular lesson, we’re going to use Little MALLET Wrapper, a Python wrapper for MALLET, to topic model a CSV file with 2,932 Reddit posts from the subreddit 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.


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 the previous lesson.

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!

Set MALLET Path

Since Little MALLET Wrapper is a Python package built around MALLET, we first need to tell it where the bigger, Java-based MALLET lives.

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.

path_to_mallet = 'mallet-2.0.8/bin/mallet'

If MALLET is located in another directory, then set your path_to_mallet to that file path.

Install Packages

#!pip install little_mallet_wrapper
#!pip install seaborn
#To install the most updated version of little_mallet_wrapper:
#!!pip install git+https://github.com/maria-antoniak/little-mallet-wrapper.git

Import Packages

Now let’s import the little_mallet_wrapper and the data viz library seaborn.

import little_mallet_wrapper
import seaborn
import glob
from pathlib import Path
import pandas as pd
import random
pd.options.display.max_colwidth = 100

We’re also going to import glob and pathlib for working with files and the file system, as well as pandas for working with the CSV data.

Get Training Data From CSV File

Note

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.

Pandas

Do you need a refresher or introduction to the Python data analysis library Pandas? Be sure to check out Pandas Basics (1-3) in this textbook!

reddit_df = pd.read_csv("../data/top-reddit-aita-posts.csv")
reddit_df.head()
author full_date date title selftext url subreddit upvote_score num_comments num_crossposts
0 Additional-Pizza-805 2020-07-24 19:13:49+00:00 2020-07-24 AITA for kicking my cousin off of my sister’s wedding Zoom call? My [27M] older sister [30F] and her fiancé [31M] were planning for over a year for their wedding... https://www.reddit.com/r/AmItheAsshole/comments/hx80wd/aita_for_kicking_my_cousin_off_of_my_sist... AmItheAsshole 11159 2209 4
1 decadel8ter 2020-07-24 14:37:13+00:00 2020-07-24 AITA for resenting my family for something that happened over a decade ago? when i was 15 i was in a car accident. i was riding my bike on new bike lanes that my city had i... https://www.reddit.com/r/AmItheAsshole/comments/hx2vvl/aita_for_resenting_my_family_for_somethin... AmItheAsshole 2541 1143 0
2 Snoo_66130 2020-07-24 12:35:35+00:00 2020-07-24 AITA for telling my step dad to stop trying to be my dad? I'm 35, and my mom who is 52 is dating a man who is is 27. This is fucking weird as hell and... https://www.reddit.com/r/AmItheAsshole/comments/hx0zk7/aita_for_telling_my_step_dad_to_stop_tryi... AmItheAsshole 2809 1253 1
3 ohnoihaveabluechair 2020-07-24 10:56:56+00:00 2020-07-24 AITA for confronting my SIL for wearing clothes that belonged to me? Some info: A few years ago, my family didn’t have a lot of spare money to buy a lot of things (l... https://www.reddit.com/r/AmItheAsshole/comments/hwzpbu/aita_for_confronting_my_sil_for_wearing_c... AmItheAsshole 7581 1550 1
4 FormalLettuce3 2020-07-24 10:52:08+00:00 2020-07-24 AITA for saying we'd only help with my ex's kid's party if we could tell people we're engaged? This guy, "Jack", and I were together for about a year, and within a couple weeks of ending it I... https://www.reddit.com/r/AmItheAsshole/comments/hwzncq/aita_for_saying_wed_only_help_with_my_exs... AmItheAsshole 2915 1214 0
reddit_df['selftext'] = reddit_df['selftext'].astype(str)

Process Reddit Posts

little_mallet_wrapper.process_string(text, numbers='remove')

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.

training_data = [little_mallet_wrapper.process_string(text, numbers='remove') for text in reddit_df['selftext']]
original_texts = [text for text in reddit_df['selftext']]

Process Reddit Post Titles

We’re also going to extract the file name for each Reddit post.

reddit_titles = [title for title in reddit_df['title']]

Get Dataset Statistics

We can get training data summary statisitcs by using the funciton little_mallet_wrapper.print_dataset_stats().

little_mallet_wrapper.print_dataset_stats(training_data)
Number of Documents: 2932
Mean Number of Words per Document: 147.3
Vocabulary Size: 19331

Training the Topic Model

Set Number of Topics

We need to make a variable num_topics and assign it the number of topics we want returned.

num_topics = 15

Set Training Data

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.

training_data = training_data

Set Other MALLET File Paths

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.”

All the other necessary variables below output_directory_path will be automatically created inside this directory.

#Change to your desired output directory
output_directory_path = 'topic-model-output/reddit'

#No need to change anything below here
Path(f"{output_directory_path}").mkdir(parents=True, exist_ok=True)

path_to_training_data           = f"{output_directory_path}/training.txt"
path_to_formatted_training_data = f"{output_directory_path}/mallet.training"
path_to_model                   = f"{output_directory_path}/mallet.model.{str(num_topics)}"
path_to_topic_keys              = f"{output_directory_path}/mallet.topic_keys.{str(num_topics)}"
path_to_topic_distributions     = f"{output_directory_path}/mallet.topic_distributions.{str(num_topics)}"

Train Topic Model

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.

little_mallet_wrapper.quick_train_topic_model(path_to_mallet,
                                             output_directory_path,
                                             num_topics,
                                             training_data)
Importing data...
Complete
Training topic model...
Complete
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   'pregnant',
   'child',
   'husband',
   'pregnancy',
   'pain',
   'birth',
   'hospital',
   'doctor',
   'could',
   'sil',
   'think',
   'abortion',
   'would',
   'medical',
   'surgery',
   'help',
   'weeks',
   'control',
   'children'],
  ['table',
   'store',
   'manager',
   'drink',
   'order',
   'restaurant',
   'bar',
   'drinking',
   'drinks',
   'pay',
   'tip',
   'place',
   'wine',
   'people',
   'coffee',
   'bottle',
   'food',
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   'beer',
   'ordered'],
  ['money',
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   'paying',
   'college',
   'job',
   'bought',
   'house',
   'gift',
   'paid',
   'car',
   'give',
   'afford',
   'since',
   'year',
   'got',
   'rent',
   'working',
   'much'],
  ['time',
   'get',
   'like',
   'want',
   'work',
   'one',
   'doesn',
   'would',
   'going',
   'take',
   'even',
   'home',
   'really',
   'think',
   'day',
   'every',
   'make',
   'feel',
   'something',
   'week'],
  ['sister',
   'mom',
   'dad',
   'family',
   'parents',
   'brother',
   'mother',
   'cousin',
   'aunt',
   'year',
   'father',
   'also',
   'house',
   'siblings',
   'older',
   'nephew',
   'always',
   'close',
   'birthday',
   'years'],
  ['school',
   'work',
   'office',
   'class',
   'name',
   'people',
   'company',
   'high',
   'teacher',
   'boss',
   'job',
   'coworkers',
   'students',
   'team',
   'college',
   'year',
   'one',
   'student',
   'english',
   'coworker'],
  ['years',
   'told',
   'would',
   'never',
   'wife',
   'life',
   'family',
   'ago',
   'relationship',
   'time',
   'know',
   'want',
   'together',
   'love',
   'months',
   'married',
   'feel',
   'even',
   'things',
   'much'],
  ['kids',
   'daughter',
   'son',
   'husband',
   'wife',
   'old',
   'child',
   'year',
   'kid',
   'name',
   'mother',
   'children',
   'mom',
   'two',
   'told',
   'school',
   'think',
   'house',
   'baby',
   'parents'],
  ['room',
   'house',
   'dog',
   'door',
   'sleep',
   'bed',
   'bathroom',
   'apartment',
   'roommate',
   'living',
   'live',
   'cat',
   'use',
   'stuff',
   'home',
   'dogs',
   'girlfriend',
   'bedroom',
   'moved',
   'place'],
  ['said',
   'told',
   'didn',
   'got',
   'asked',
   'like',
   'really',
   'went',
   'back',
   'one',
   'friend',
   'could',
   'friends',
   'get',
   'know',
   'started',
   'wasn',
   'would',
   'left',
   'aita'],
  ['wedding',
   'dress',
   'hair',
   'wear',
   'ring',
   'married',
   'fiancé',
   'party',
   'wearing',
   'one',
   'would',
   'think',
   'tattoo',
   'family',
   'husband',
   'getting',
   'makeup',
   'engaged',
   'fiance',
   'ceremony'],
  ['people',
   'like',
   'girls',
   'gay',
   'think',
   'girl',
   'women',
   'said',
   'always',
   'group',
   'girlfriend',
   'friends',
   'woman',
   'one',
   'white',
   'doesn',
   'really',
   'look',
   'know',
   'thing'],
  ['car',
   'seat',
   'back',
   'get',
   'minutes',
   'around',
   'front',
   'woman',
   'guy',
   'next',
   'move',
   'people',
   'away',
   'flight',
   'door',
   'phone',
   'lady',
   'sit',
   'park',
   'drive'],
  ['food',
   'eat',
   'eating',
   'vegan',
   'dinner',
   'make',
   'meat',
   'cook',
   'weight',
   'cooking',
   'meal',
   'ate',
   'chicken',
   'lunch',
   'fridge',
   'eats',
   'diet',
   'cake',
   'family',
   'hungry'],
  ['phone',
   'facebook',
   'post',
   'pictures',
   'social',
   'account',
   'game',
   'sent',
   'picture',
   'video',
   'posted',
   'media',
   'call',
   'also',
   'message',
   'messages',
   'number',
   'instagram',
   'photos',
   'sarah']],
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   0.21038699836141783,
   0.0004190064662620541,
   0.025602883034298494,
   0.0007271664968628483,
   0.0003819492520499683,
   0.0006318426224995765],
  [0.0010171224827424186,
   0.0008390570464820244,
   0.0020132725349401117,
   0.10389599126982152,
   0.009906340203169994,
   0.00893529579333834,
   0.045169665339854025,
   0.0025827051515788157,
   0.11733942548788612,
   0.3464593815879142,
   0.0007976992738773531,
   0.3577296290387727,
   0.0013843704888617462,
   0.0007271502125881397,
   0.0012028940881724994],
  [0.0021082597544981705,
   0.0017391712727228455,
   0.004173048509169224,
   0.3594556423112671,
   0.00452207823641992,
   0.16262407489528447,
   0.09362627331115789,
   0.005353350674274337,
   0.0030453800782978178,
   0.20576300772602252,
   0.0016534461717664481,
   0.1490662514717793,
   0.0028694799657882518,
   0.0015072142782066437,
   0.002493321343345123],
  [0.19898136984151144,
   0.0006942581586640128,
   0.0016658353432066622,
   0.009267043726895912,
   0.15520370013983747,
   0.0010016961743994086,
   0.414479318939821,
   0.03409502738584935,
   0.02678210509132797,
   0.08213834318996507,
   0.013443248964319726,
   0.008372772612765686,
   0.00753707069627584,
   0.0006016634623119188,
   0.045736546272848524],
  [0.006881337541844437,
   0.0006604856481826538,
   0.062391630400824814,
   0.10610717270816354,
   0.09292759673120427,
   0.061759798590252706,
   0.029475797673676158,
   0.002033043752218531,
   0.0011565449973354157,
   0.3335313729912835,
   0.0006279297983024605,
   0.014046158240565569,
   0.2868818461929399,
   0.0005723952638275365,
   0.0009468894693785477],
  [0.0009510064990138774,
   0.0007845158452267454,
   0.08855303246621082,
   0.18381307174174846,
   0.06704281773917377,
   0.001131922630094234,
   0.2589100744434571,
   0.03130503118509972,
   0.0013737283749070063,
   0.17948744457654567,
   0.0007458464507348127,
   0.08890935905405402,
   0.0012943822934685322,
   0.09457306430218927,
   0.0011247023980760057],
  [0.0008751561641214331,
   0.0007219444646414595,
   0.0017322671545501782,
   0.23561748045213607,
   0.0018771522981447242,
   0.03427412463113462,
   0.23161343964493702,
   0.1019196659134993,
   0.09431511175018571,
   0.23163685646981447,
   0.04721183383359051,
   0.0020601738619330273,
   0.014484137741661922,
   0.0006256571864117162,
   0.0010349984332378482],
  [0.0013651082709869133,
   0.19810874400143502,
   0.002702068861743506,
   0.1705442083982888,
   0.0029280672789089683,
   0.001624801666509275,
   0.02952094030366572,
   0.003466320156874962,
   0.0019718981611792337,
   0.3405826600138568,
   0.0010706143016268376,
   0.0032135526136859785,
   0.0018580019973208604,
   0.21869356361078332,
   0.022349450363133856],
  [0.1338431498235277,
   0.012846554761838653,
   0.030824621495110654,
   0.17147740106794468,
   0.03340276292083453,
   0.018535388599407066,
   0.10022797397542552,
   0.03954303620063915,
   0.022494990895988647,
   0.21891494960507782,
   0.01221333811367245,
   0.03665951833203013,
   0.021195687909900382,
   0.011133182261280586,
   0.13668744403732208],
  ...])

When the topic model finishes, it will output your results to your output_directory_path.

Display Topics and Top Words

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”.

topics = little_mallet_wrapper.load_topic_keys(path_to_topic_keys)

for topic_number, topic in enumerate(topics):
    print(f"✨Topic {topic_number}\n\n{topic}\n")
✨Topic 0✨

['baby', 'pregnant', 'child', 'husband', 'pregnancy', 'pain', 'birth', 'hospital', 'doctor', 'could', 'sil', 'think', 'abortion', 'would', 'medical', 'surgery', 'help', 'weeks', 'control', 'children']

✨Topic 1✨

['table', 'store', 'manager', 'drink', 'order', 'restaurant', 'bar', 'drinking', 'drinks', 'pay', 'tip', 'place', 'wine', 'people', 'coffee', 'bottle', 'food', 'alcohol', 'beer', 'ordered']

✨Topic 2✨

['money', 'pay', 'buy', 'would', 'paying', 'college', 'job', 'bought', 'house', 'gift', 'paid', 'car', 'give', 'afford', 'since', 'year', 'got', 'rent', 'working', 'much']

✨Topic 3✨

['time', 'get', 'like', 'want', 'work', 'one', 'doesn', 'would', 'going', 'take', 'even', 'home', 'really', 'think', 'day', 'every', 'make', 'feel', 'something', 'week']

✨Topic 4✨

['sister', 'mom', 'dad', 'family', 'parents', 'brother', 'mother', 'cousin', 'aunt', 'year', 'father', 'also', 'house', 'siblings', 'older', 'nephew', 'always', 'close', 'birthday', 'years']

✨Topic 5✨

['school', 'work', 'office', 'class', 'name', 'people', 'company', 'high', 'teacher', 'boss', 'job', 'coworkers', 'students', 'team', 'college', 'year', 'one', 'student', 'english', 'coworker']

✨Topic 6✨

['years', 'told', 'would', 'never', 'wife', 'life', 'family', 'ago', 'relationship', 'time', 'know', 'want', 'together', 'love', 'months', 'married', 'feel', 'even', 'things', 'much']

✨Topic 7✨

['kids', 'daughter', 'son', 'husband', 'wife', 'old', 'child', 'year', 'kid', 'name', 'mother', 'children', 'mom', 'two', 'told', 'school', 'think', 'house', 'baby', 'parents']

✨Topic 8✨

['room', 'house', 'dog', 'door', 'sleep', 'bed', 'bathroom', 'apartment', 'roommate', 'living', 'live', 'cat', 'use', 'stuff', 'home', 'dogs', 'girlfriend', 'bedroom', 'moved', 'place']

✨Topic 9✨

['said', 'told', 'didn', 'got', 'asked', 'like', 'really', 'went', 'back', 'one', 'friend', 'could', 'friends', 'get', 'know', 'started', 'wasn', 'would', 'left', 'aita']

✨Topic 10✨

['wedding', 'dress', 'hair', 'wear', 'ring', 'married', 'fiancé', 'party', 'wearing', 'one', 'would', 'think', 'tattoo', 'family', 'husband', 'getting', 'makeup', 'engaged', 'fiance', 'ceremony']

✨Topic 11✨

['people', 'like', 'girls', 'gay', 'think', 'girl', 'women', 'said', 'always', 'group', 'girlfriend', 'friends', 'woman', 'one', 'white', 'doesn', 'really', 'look', 'know', 'thing']

✨Topic 12✨

['car', 'seat', 'back', 'get', 'minutes', 'around', 'front', 'woman', 'guy', 'next', 'move', 'people', 'away', 'flight', 'door', 'phone', 'lady', 'sit', 'park', 'drive']

✨Topic 13✨

['food', 'eat', 'eating', 'vegan', 'dinner', 'make', 'meat', 'cook', 'weight', 'cooking', 'meal', 'ate', 'chicken', 'lunch', 'fridge', 'eats', 'diet', 'cake', 'family', 'hungry']

✨Topic 14✨

['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']

Load Topic Distributions

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.

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”.

topic_distributions = little_mallet_wrapper.load_topic_distributions(path_to_topic_distributions)
topic_distributions[0]
[0.011076196974682066,
 0.000564392649472897,
 0.0013542299952905628,
 0.1530219806345308,
 0.1521519084626053,
 0.000814322382504041,
 0.08234356736699737,
 0.006933273680468054,
 0.000988281118714402,
 0.33177055578329395,
 0.2551412694103212,
 0.0016105767704565042,
 0.0009311983390601434,
 0.0004891183939972939,
 0.000809128037605374]

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.

reddit_post_to_check = "AITA for walking my cat on a trail frequented by dogs?"

reddit_post_number = reddit_titles.index(reddit_post_to_check)

print(f"Topic Distributions for {reddit_titles[reddit_post_number]}\n")
for topic_number, (topic, topic_distribution) in enumerate(zip(topics, topic_distributions[reddit_post_number])):
    print(f"✨Topic {topic_number} {topic[:6]}\nProbability: {round(topic_distribution, 3)}\n")
Topic Distributions for AITA for walking my cat on a trail frequented by dogs?

✨Topic 0 ['baby', 'pregnant', 'child', 'husband', 'pregnancy', 'pain'] ✨
Probability: 0.002

✨Topic 1 ['table', 'store', 'manager', 'drink', 'order', 'restaurant'] ✨
Probability: 0.002

✨Topic 2 ['money', 'pay', 'buy', 'would', 'paying', 'college'] ✨
Probability: 0.024

✨Topic 3 ['time', 'get', 'like', 'want', 'work', 'one'] ✨
Probability: 0.177

✨Topic 4 ['sister', 'mom', 'dad', 'family', 'parents', 'brother'] ✨
Probability: 0.024

✨Topic 5 ['school', 'work', 'office', 'class', 'name', 'people'] ✨
Probability: 0.003

✨Topic 6 ['years', 'told', 'would', 'never', 'wife', 'life'] ✨
Probability: 0.035

✨Topic 7 ['kids', 'daughter', 'son', 'husband', 'wife', 'old'] ✨
Probability: 0.006

✨Topic 8 ['room', 'house', 'dog', 'door', 'sleep', 'bed'] ✨
Probability: 0.509

✨Topic 9 ['said', 'told', 'didn', 'got', 'asked', 'like'] ✨
Probability: 0.091

✨Topic 10 ['wedding', 'dress', 'hair', 'wear', 'ring', 'married'] ✨
Probability: 0.002

✨Topic 11 ['people', 'like', 'girls', 'gay', 'think', 'girl'] ✨
Probability: 0.006

✨Topic 12 ['car', 'seat', 'back', 'get', 'minutes', 'around'] ✨
Probability: 0.022

✨Topic 13 ['food', 'eat', 'eating', 'vegan', 'dinner', 'make'] ✨
Probability: 0.02

✨Topic 14 ['phone', 'facebook', 'post', 'pictures', 'social', 'account'] ✨
Probability: 0.078

Explore Heatmap of Topics and Texts

We can visualize and compare these topic probability distributions with a heatmap by using the little_mallet_wrapper.plot_categories_by_topics_heatmap() function.

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.

target_labels = random.sample(reddit_titles, 6)
little_mallet_wrapper.plot_categories_by_topics_heatmap(reddit_titles,
                                      topic_distributions,
                                      topics, 
                                      output_directory_path + '/categories_by_topics.pdf',
                                      target_labels=target_labels,
                                      dim= (18, 8)
                                     )
../_images/10-Topic-Modeling-CSV_62_0.png

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!

from IPython.display import IFrame
IFrame("topic-model-output/reddit/categories_by_topics.pdf", width=1000, height=600)

Display Top Titles Per Topic

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.

training_data_reddit_titles = dict(zip(training_data, reddit_titles))
training_data_original_text = dict(zip(training_data, original_texts))

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.

def display_top_titles_per_topic(topic_number=0, number_of_documents=5):
    
    print(f"✨Topic {topic_number}\n\n{topics[topic_number]}\n")

    for probability, document in little_mallet_wrapper.get_top_docs(training_data, topic_distributions, topic_number, n=number_of_documents):
        print(round(probability, 4), training_data_reddit_titles[document] + "\n")
    return

Topic 0

To display the top 5 Reddit post titles with the highest probability of containing Topic 0, we will run:

display_top_titles_per_topic(topic_number=0, number_of_documents=5)
✨Topic 0✨

['baby', 'pregnant', 'child', 'husband', 'pregnancy', 'pain', 'birth', 'hospital', 'doctor', 'could', 'sil', 'think', 'abortion', 'would', 'medical', 'surgery', 'help', 'weeks', 'control', 'children']

0.4107 AITA for offering my DNA because my brother won't do a paternity test

0.378 AITA for causing a scene in the hospital over an IV?

0.3728 AITA for lying about my birth control use?

0.363 AITA for asking my immediate family to get the flu shot if they want to see our newborn in the hospital

0.3542 AITA for feeling jealous that my sister is getting more attention for her illness than I am for mine?

What would you label this topic?

To display the top 5 Reddit post titles with the highest probability of containing Topic 9, we will run:

display_top_titles_per_topic(topic_number=10, number_of_documents=5)
✨Topic 10✨

['wedding', 'dress', 'hair', 'wear', 'ring', 'married', 'fiancé', 'party', 'wearing', 'one', 'would', 'think', 'tattoo', 'family', 'husband', 'getting', 'makeup', 'engaged', 'fiance', 'ceremony']

0.4522 AITA for showing up at my cousin's wedding in a hoodie and jean shorts?

0.438 AITA? I wore an accessory ring to work knowing that my co-worker has the same ring for her engagement ring.

0.4347 AITA for not including a bridesmaid in photographs because she wore a suit?

0.4049 AITA for dressing nicer than the couple getting married?

0.3982 AITA for getting a tattoo before my brother's wedding?

What would you label this topic?

To display the top 7 Reddit posts with the highest probability of containing Topic 8, we will run:

display_top_titles_per_topic(topic_number=8, number_of_documents=7)
✨Topic 8✨

['room', 'house', 'dog', 'door', 'sleep', 'bed', 'bathroom', 'apartment', 'roommate', 'living', 'live', 'cat', 'use', 'stuff', 'home', 'dogs', 'girlfriend', 'bedroom', 'moved', 'place']

0.5773 AITA for refusing to eat my GF cooking because her cats litter is disgusting?

0.5279 AITA for peeing in places that are not the toilet?

0.5087 AITA for walking my cat on a trail frequented by dogs?

0.5039 Aita for not wanting all 3 of my triplet children to have to share a room

0.4808 WIBTA if I told my roommate his girlfriend is getting too comfortable in our home?

0.4544 AITA for turning off the dryer on someone who removed my laundry before it was done?

0.4374 AITA for not allowing my roommate's girlfriend to use my bathroom causing her to piss herself

What would you label this topic?

Display Topic Words in Context of Original Text

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.

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().

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.

from IPython.display import Markdown, display
import re

def display_bolded_topic_words_in_context(topic_number=3, number_of_documents=3, custom_words=None):

    for probability, document in little_mallet_wrapper.get_top_docs(training_data, topic_distributions, topic_number, n=number_of_documents):
        
        print(f"✨Topic {topic_number}\n\n{topics[topic_number]}\n")
        
        probability = f"✨✨✨\n\n**{probability}**"
        reddit_title = f"**{training_data_reddit_titles[document]}**"
        original_text = training_data_original_text[document]
        topic_words = topics[topic_number]
        topic_words = custom_words if custom_words != None else topic_words

        for word in topic_words:
            if word in original_text:
                original_text = re.sub(f"\\b{word}\\b", f"**{word}**", original_text)

        display(Markdown(probability)), display(Markdown(reddit_title)), display(Markdown(original_text))
    return

Topic 3

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:

display_bolded_topic_words_in_context(topic_number=3, number_of_documents=3)
✨Topic 3✨

['time', 'get', 'like', 'want', 'work', 'one', 'doesn', 'would', 'going', 'take', 'even', 'home', 'really', 'think', 'day', 'every', 'make', 'feel', 'something', 'week']

✨✨✨

0.7187352724737381

AITA for setting my boyfriend up to fail?

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.

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.

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.

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?

✨Topic 3✨

['time', 'get', 'like', 'want', 'work', 'one', 'doesn', 'would', 'going', 'take', 'even', 'home', 'really', 'think', 'day', 'every', 'make', 'feel', 'something', 'week']

✨✨✨

0.674436146664204

AITA for complaining she is constantly making plans for both of us?

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.

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.

There are two more layers to this.

  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.

  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.

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.

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.

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?

✨Topic 3✨

['time', 'get', 'like', 'want', 'work', 'one', 'doesn', 'would', 'going', 'take', 'even', 'home', 'really', 'think', 'day', 'every', 'make', 'feel', 'something', 'week']

✨✨✨

0.6179777159182313

AITA for reporting a pregnant coworker to management after discovering we’re all covering for her?

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. 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. 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. 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.

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.

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. 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.

Topic 8

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:

display_bolded_topic_words_in_context(topic_number=14, number_of_documents=5)
✨Topic 14✨

['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']

✨✨✨

0.44218633047423606

AITA for suing over not winning a contest?

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.

​

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

​

There were 5 prizes:

  1. $3000 + mocap suit

  2. $2000 + mocap suit

  3. $1000 + mocap suit

  4. mocap suit

  5. mocap suit

​

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.

​

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.

​

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.

​

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.

​

Instead someone with 7 votes got 1st place. Someone with 14 votes got 2nd, and so on.

​

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".

​

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"

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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.

​

I gave them an opportunity to fix it, but they said they stand by their decision.

​

So, I am suing them for the prize I believe I am owed.

AITA?

✨Topic 14✨

['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']

✨✨✨

0.41619810812974495

AITA for deleting a video off my friends phone.

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.

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. I thought that because I was around friends it would be ok.

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.

she got pissed and left, and told me that I have no right to take her phone and delete her video. I'm now thinking I overstepped.

AITA?

✨Topic 14✨

['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']

✨✨✨

0.39980537706581076

AITA for messing with my sister's fake profile?

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.

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.

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.

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?

✨Topic 14✨

['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']

✨✨✨

0.39082206926846264

[Update} WIBTAA if I take only one dog for a walk.

Original post

https://www.reddit.com/r/AmItheAsshole/comments/choiv4/wibta_if_i_started_taking_only_one_of_my_dogs_for/

Update. Baxter couldn't do stairs or lay down without hurting.. even when I gave him drugs.

​

I let him go this morning. He is with Lincoln, Indigo and Winston now. I have never cried so much.

✨Topic 14✨

['phone', 'facebook', 'post', 'pictures', 'social', 'account', 'game', 'sent', 'picture', 'video', 'posted', 'media', 'call', 'also', 'message', 'messages', 'number', 'instagram', 'photos', 'sarah']

✨✨✨

0.38733474788555244

AITA Changing someone’s instagram password for using my email

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.

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.”

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.”