Large-Scale NLP Sentiment Analysis Pipeline

Problem statement

Evaluating sentiment at scale required balancing model complexity with training time and infrastructure cost. Transformer-based models delivered strong results but introduced significant computational overhead.

Architecture overview

A batch pipeline processed ~100k Amazon reviews using multiple NLP approaches including Bag-of-Words, LSTM, and BERT. Training and evaluation were orchestrated on AWS using SageMaker and S3-backed datasets.

Technical decisions & tradeoffs

• Froze lower transformer layers to reduce training cost while preserving accuracy.
• Compared simpler models as baselines to justify infrastructure expense.
• Optimized for reproducibility and evaluation clarity over experimentation speed.

Lessons learned

Model performance must be evaluated alongside operational cost. Simpler architectures often provide better end-to-end value in production systems.