Movie Review Sentiment Analysis (MapReduce + ML)

Description

A comprehensive MLOps project for sentiment analysis of movie reviews using Apache Spark MapReduce and machine learning. Includes Spark-based TF-IDF feature extraction, MLlib models (Naive Bayes, Logistic Regression, Random Forest), PyTorch deep learning variants (LSTM/Transformer/BERT), MLflow tracking, Docker, and Makefile-driven workflows.

Challenges & Solutions

Designing a unified pipeline supporting both Spark MLlib and PyTorch while maintaining reproducibility and comparability. Implementing TF-IDF at scale, robust experiment tracking with MLflow, and containerized, repeatable training/inference flows. Balancing performance and resource usage across distributed and GPU workloads.

Technical Achievements

• Hybrid Pipeline: Unified Spark MapReduce feature engineering with parallel PyTorch deep learning path
• Model Zoo: Implemented Naive Bayes, Logistic Regression, Random Forest, plus LSTM/Transformer/BERT variants
• Experiment Tracking: Full MLflow integration with metrics, params, and artifact logging
• Reproducibility: Makefile + Docker workflows for install, train, evaluate, and predict
• Scalability: Distributed TF-IDF and training with Spark, GPU-ready PyTorch training scripts
• CI-ready Structure: Config-driven scripts, tests, and modular code layout for extension

Technologies Used