A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets

TL;DR

This paper introduces TRABSA, a hybrid transformer and attention-based RNN framework that achieves state-of-the-art sentiment analysis accuracy on tweets, with enhanced interpretability and robustness across diverse datasets.

Contribution

The paper presents a novel hybrid model combining transformers, attention, and BiLSTM for improved, interpretable sentiment analysis of tweets, leveraging extensive datasets and advanced explainability techniques.

Findings

TRABSA achieves 94% accuracy, outperforming traditional models.

Incorporating diverse datasets improves model generalizability.

SHAP and LIME enhance interpretability of sentiment predictions.

Abstract

Sentiment analysis is crucial for understanding public opinion and consumer behavior. Existing models face challenges with linguistic diversity, generalizability, and explainability. We propose TRABSA, a hybrid framework integrating transformer-based architectures, attention mechanisms, and BiLSTM networks to address this. Leveraging RoBERTa-trained on 124M tweets, we bridge gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy. Augmenting datasets with tweets from 32 countries and US states, we compare six word-embedding techniques and three lexicon-based labeling techniques, selecting the best for optimal sentiment analysis. TRABSA outperforms traditional ML and deep learning models with 94% accuracy and significant precision, recall, and F1-score gains. Evaluation across diverse datasets demonstrates consistent superiority and generalizability. SHAP and LIME analyses enhance interpretability, improving confidence in predictions. Our study facilitates pandemic resource management, aiding resource planning, policy formation, and vaccination tactics.