Reddit Topic Modeling

 Reddit Topic Modeling

1. Introduction

Objective: Use Natural Language Processing (NLP) and Latent Dirichlet Allocation (LDA) to uncover hidden topics in Reddit discussions.
Purpose: Gain insights into prevalent themes and trends within specific subreddits or across the Reddit platform.

2. Project Workflow

1. Problem Definition:
   - Extract topics from Reddit posts and comments using NLP techniques.
   - Key questions:
     - What are the dominant topics in a subreddit?
     - How do these topics evolve over time?
2. Data Collection:
   - Source: Reddit API (PRAW) for fetching posts and comments.
   - Example fields: Post Title, Content, Comments, Timestamp.
3. Data Preprocessing:
   - Tokenization, stopword removal, and lemmatization.
4. Topic Modeling:
   - Use LDA to extract latent topics and their distributions.
5. Visualization:
   - Use word clouds and inter-topic distance maps for presentation.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - NLP: NLTK, spaCy, Gensim
  - Visualization: Matplotlib, PyLDAvis, WordCloud
  - API Interaction: PRAW (Python Reddit API Wrapper)

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy nltk spacy gensim praw matplotlib pyldavis wordcloud
```
Download spaCy model:
```
python -m spacy download en_core_web_sm
```

Step 2: Data Collection

Fetch data from Reddit using PRAW:
```
import praw

reddit = praw.Reddit(client_id='YOUR_CLIENT_ID',
                     client_secret='YOUR_CLIENT_SECRET',
                     user_agent='YOUR_USER_AGENT')

subreddit = reddit.subreddit("learnpython")
posts = []
for post in subreddit.top(limit=100):
    posts.append({'title': post.title, 'selftext': post.selftext, 'comments': [comment.body for comment in post.comments]})
```
Convert to a Pandas DataFrame for analysis:
```
import pandas as pd

df = pd.DataFrame(posts)
```

Step 3: Preprocess Data

Clean and preprocess text data:
- Tokenize, remove stopwords, and lemmatize:
```
import spacy
from nltk.corpus import stopwords

nlp = spacy.load("en_core_web_sm")
stop_words = set(stopwords.words('english'))

def preprocess_text(text):
    doc = nlp(text)
    tokens = [token.lemma_ for token in doc if token.is_alpha and token.text.lower() not in stop_words]
    return tokens

df['processed'] = df['selftext'].apply(preprocess_text)
```

Step 4: Topic Modeling with LDA

Use Gensim to perform LDA:
```
from gensim.corpora import Dictionary
from gensim.models import LdaModel

dictionary = Dictionary(df['processed'])
corpus = [dictionary.doc2bow(text) for text in df['processed']]

lda_model = LdaModel(corpus=corpus, id2word=dictionary, num_topics=5, random_state=42, passes=10)
```
Display topics:
```
topics = lda_model.print_topics(num_words=5)
for topic in topics:
    print(topic)
```

Step 5: Visualize Results

Create visualizations:
1. Word Clouds:
```
from wordcloud import WordCloud

for i, topic in lda_model.show_topics(formatted=False, num_words=20):
    wordcloud = WordCloud(width=800, height=400).fit_words(dict(topic))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis("off")
    plt.title(f"Topic {i}")
    plt.show()
```
2. Inter-topic Distance Map:
```
import pyLDAvis
import pyLDAvis.gensim_models as gensimvis

lda_vis = gensimvis.prepare(lda_model, corpus, dictionary)
pyLDAvis.show(lda_vis)
```

5. Expected Outcomes

1. Identification of latent topics within Reddit data.
2. Visualizations of word distributions for each topic.
3. Insights into the thematic composition and trends of the subreddit.

6. Additional Suggestions

- Time-based Analysis:
  - Analyze how topic distributions change over time within a subreddit.
- Sentiment Analysis:
  - Combine with sentiment analysis to understand emotional tones associated with topics.
- Deployment:
  - Develop an interactive dashboard to explore topics and visualize trends.