Topic Modeling with Latent Dirichlet Allocation (LDA) using Sklearn

This project demonstrates how to perform topic modeling on a small corpus using Latent Dirichlet Allocation (LDA) implemented in scikit-learn. It includes end-to-end steps starting from text preprocessing to extracting interpretable topics from the documents.

Key Features

📂 Loading and combining multiple text documents as corpus.

🧹 Text preprocessing: tokenization, stopword removal, punctuation cleaning, and lemmatization.

📊 Feature extraction using TF-IDF and CountVectorizer.

🧠 Topic extraction using LDA (LatentDirichletAllocation) from sklearn.decomposition.

📌 Visualizing topics and their top contributing words.

🧾 Assigning the most probable topic to each document.

Example Output

Topics Identified:

Topic 1: ['movie', 'good', 'watch', 'book', 'weekend']

Topic 2: ['zealand', 'test', 'beating', 'world', 'championship']

Topic 3: ['weekend', 'want', 'watch', 'movie', 'book'] ...

Document-to-Topic Mapping

Document 1 --> Topic 2

Document 2 --> Topic 1

...

Technologies Used

• Python

• Scikit-learn

• NLTK

• NumPy, Pandas

Use Cases in Development & Live Environments

Development Environment

✅ Prototype NLP pipelines for document classification or clustering.

✅ Quickly test topic discovery techniques on small sets of articles, customer reviews, chat transcripts, etc.

✅ Evaluate and compare performance of TF-IDF vs CountVectorizer for different domains (e.g., e-commerce reviews vs. news headlines).

✅ Validate text cleaning pipelines (tokenization, lemmatization, etc.) before applying to large datasets.

✅ Integrate into Jupyter notebooks for exploratory data analysis (EDA).

Live (Production) Environment

📑 Content Categorization: Automatically tag support tickets, feedback forms, or blog posts with relevant topics.

📰 News Aggregation: Group similar articles into topic clusters for personalized content feeds.

🛒 E-commerce: Detect dominant topics in customer reviews or Q&A to highlight key product features/issues.

🤖 Chatbots & Virtual Assistants: Use topic modeling as a fallback to classify ambiguous or unknown intents.

📊 Data Dashboarding: Embed topic trends over time into dashboards for product teams or customer service analytics.