ADVANCED-SENTIMENT-ANALYSIS-USING-BERT-AND-LSTM.MD
NLP + DEEP LEARNING
Advanced Sentiment Analysis using BERT & LSTM
A comparative NLP project that fine-tunes BERT and benchmarks it against deep learning baselines for sentiment classification.
Overview
This project explores advanced sentiment analysis using both transformer-based and neural-network-based approaches. The focus was on model quality, benchmarking discipline, and understanding how different architectures behave on the same task.
Problem
Sentiment analysis can be approached with both modern transformer models and more traditional sequence models, but comparative evaluation is important to understand what actually delivers the strongest results.
Role
I handled preprocessing, model training, evaluation, and cross-model comparison, with a focus on benchmarking rather than treating a single model as the answer by default.
Implementation
- Fine-tuned a pre-trained BERT model for sentiment classification.
- Built comparison baselines using Bi-LSTM and CNN architectures.
- Evaluated model quality through structured preprocessing and comparative testing.
- Focused on understanding why some models perform better, not only what their final scores are.
Outcomes
- Achieved 92.6% accuracy with the BERT-based workflow.
- Outperformed 2 baseline models by 12%.
- Strengthened my deep learning and NLP benchmarking skills through comparative analysis.