Sentiment Analysis & Movie Recommendation
Combined sentiment analysis with clustering for more relevant movie recommendations using IMDB reviews.
Overview
Developed an NLP-driven system that processes 10,000+ IMDB reviews to classify sentiment and improve recommendation quality. By combining traditional text processing with clustering, the system produced more personalized movie suggestions.
Approach
- TF-IDF Vectorization to convert text into features.
- K-Means Clustering to group reviews by sentiment patterns.
- PCA for dimensionality reduction and faster runtime.
- Hyperparameter Tuning for accuracy optimization.
Results
- Improved sentiment classification precision and reduced runtime by 50%.
- Recommendations better aligned with user preferences.
Skills demonstrated: NLP (TF-IDF), clustering, dimensionality reduction, model tuning.