This project performs topic modeling on the Amazon Reviews Dataset to extract meaningful themes, customer sentiments, and product-related insights from large-scale textual review data. It compares two powerful topic modeling approaches:
- Latent Dirichlet Allocation (LDA) – A traditional probabilistic topic modeling technique
- BERTopic – A transformer-based modern topic modeling framework
The goal is to evaluate how classical and contextual NLP methods differ in topic coherence, interpretability, and semantic understanding when applied to real-world e-commerce reviews.
Amazon Reviews Dataset: https://www.kaggle.com/datasets/kritanjalijain/amazon-reviews
This dataset contains large volumes of customer reviews across products, making it ideal for:
- Customer sentiment exploration
- Product trend identification
- Hidden theme extraction
- NLP benchmarking
LDA is a generative probabilistic model that identifies hidden topics based on word distributions across documents.
- Text cleaning and preprocessing
- Tokenization
- Stopword removal
- Vectorization using Bag-of-Words / TF-IDF
- Topic extraction using probabilistic distributions
- Interpretable topic-word distributions
- Strong baseline for topic modeling
- Efficient for large corpora
- Limited semantic understanding
- Sensitive to preprocessing
- Less effective on short or context-rich text
BERTopic combines:
- Transformer embeddings (BERT)
- UMAP dimensionality reduction
- HDBSCAN clustering
- Class-based TF-IDF
This approach captures contextual and semantic relationships beyond simple word frequency.
- Context-aware topic extraction
- Higher topic coherence
- Better performance on diverse review texts
- More meaningful semantic clusters
- Higher computational cost
- Larger model size
- More resource intensive
- Compare LDA and BERTopic performance
- Analyze topic coherence and semantic quality
- Visualize extracted topics
- Understand customer concerns and product themes
- Benchmark traditional vs transformer-based NLP methods
- Python
- BERTopic
- Scikit-learn
- Gensim
- Pandas
- NumPy
- Matplotlib / Seaborn
- HuggingFace Transformers
- Produced interpretable keyword-based topics
- Useful for broad trend analysis
- Lower contextual accuracy
- Generated semantically richer topics
- Better clustered nuanced customer concerns
- Superior topic coherence and modern NLP performance
- Kaggle Amazon Reviews Dataset: https://www.kaggle.com/datasets/kritanjalijain/amazon-reviews
- Gensim Documentation: https://radimrehurek.com/gensim/models/ldamodel.html
This project demonstrates the evolution of topic modeling from traditional statistical methods like LDA to modern embedding-based approaches like BERTopic. While LDA offers simplicity and interpretability, BERTopic provides significantly improved semantic understanding and topic quality for large-scale customer review analysis.
- Fine-tune transformer models on review-specific corpora
- Add sentiment-topic correlation analysis
- Implement dynamic topic tracking over time
- Deploy interactive dashboard for topic exploration
Developed as an NLP and machine learning project to explore advanced topic modeling techniques on real-world Amazon review datasets.