Hybrid Approach Using RoBERTa and Neural Networks for Airline Twitter Sentiment Analysis

The proliferation of social media platforms like Twitter has generated vast datasets ripe for sentiment analysis. This paper introduces a novel approach that combines RoBERTa, an advanced deep learning (DL) model, with neural network models for airline Twitter sentiment analysis. By utilizing RoBERTa’s robust contextual language representations and integrating these with feedforward neural networks (FNNs) and multi-layer perceptrons (MLPs), the proposed hybrid approach captures nuanced sentiment patterns effectively. Experimental results on an airline Twitter sentiment dataset reveal that the hybrid integration achieves an accuracy of 97.1%, outperforming standalone methods. Comprehensive evaluation metrics validate the superiority of this hybrid framework in sentiment classification tasks.