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Enhanced Sentiment Analysis Using RoBERTa and BiLSTM: A Context-Aware Hybrid Deep Learning Approach

V. Gayatri Rajani Rajalingam
Jul 2026 · International Journal for Research in Applied Science and Engineering Technology · 0 citations

TL;DR

This paper presents a context-aware hybrid deep learning approach by integrating the Robustly Optimized BERT Pretraining Approach (RoBERTa) with Bidirectional Long Short-Term Memory (BiLSTM) networks to generate rich contextual word embeddings.

Abstract

With the widespread growth of digital platforms, online interaction has become an essential part of everyday life. Users frequently express their opinions, feedback, and emotions through reviews and comments on various platforms. Analyzing such textual data plays a crucial role in understanding user sentiment and supporting effective decision-making. However, sentiment analysis faces several challenges, including long-range dependencies within text and the presence of unknown words and symbols. Traditional sentiment analysis approaches mainly rely on sequential models, which process text step by step and often require higher computational time. In contrast, Transformer-based models offer improved efficiency through parallel processing. To address these challenges, this paper presents a context-aware hybrid deep learning approach by integrating the Robustly Optimized BERT Pretraining Approach (RoBERTa) with Bidirectional Long Short-Term Memory (BiLSTM) networks. RoBERTa is employed to generate rich contextual word embeddings, while BiLSTM captures long-term semantic dependencies by processing text in both forward and backward directions. The proposed model is trained and evaluated on the Twitter US Airline Sentiment dataset comprising 14,299 samples across three sentiment classes. Experimental analysis demonstrates that the hybrid approach achieves an accuracy of 85.14% and an F1-score of 0.8487, highlighting its effectiveness for sentiment analysis tasks compared to baseline models

DOI Semantic Scholar
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