RoBERTa-BiLSTM: A Context-Aware Hybrid Model for Sentiment Analysis

Project Code :TCMAPY1732

Objective

This project introduces a context-aware sentiment analysis system using three deep learning models—BiLSTM, RoBERTa-BiLSTM, and DistilBERT+GRU—trained on IMDb and Twitter datasets. The models classify user input as positive or negative by capturing contextual semantics. To enhance transparency, LIME (Local Interpretable Model-agnostic Explanations) provides visual insights into predictions. A responsive web interface enables users to log in, input text, and receive real-time sentiment predictions with explanations. By combining advanced NLP with explainable AI and an intuitive UI, the system improves user trust and is well-suited for review analysis, social media monitoring, and public sentiment tracking.

Abstract

This project presents a context-aware sentiment analysis framework utilizing three powerful models—BiLSTM, RoBERTa-BiLSTM, and DistilBERT+GRU—on two benchmark datasets: IMDb and Twitter. Each model is trained to classify user-submitted text as positive or negative by effectively capturing contextual and semantic nuances. To enhance interpretability, we integrate Explainable AI techniques using LIME (Local Interpretable Model-agnostic Explanations), providing users with a visual explanation of the model’s decision-making process. The system features a user-friendly web interface developed using HTML, CSS, and JavaScript. Authenticated users can log in, submit input text, and receive real-time predictions along with LIME-based explanations. The integration of advanced NLP models with explainability and intuitive UI bridges the gap between deep learning and end-user trust, making this tool highly useful for applications in social media monitoring, review analysis, and public sentiment tracking.

Keywords:Sentiment Analysis, RoBERTa-BiLSTM, BiLSTM, DistilBERT+GRU, LIME, Explainable AI, IMDb Dataset, Twitter Dataset, NLP, Context-Aware Models

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.