Enhanced twitter sentiment analysis with dual joint classifier integrating RoBERTa and BERT architectures - He, Luoyao (Frontiers in Physics, 2024)
Sentiment analysis, a crucial aspect of Natural Language Processing (NLP), aims to extract subjective information from textual data. With the proliferation of social media platforms like Twitter, accurately determining public sentiment has become increasingly important for businesses, policymakers, and researchers. This study introduces the Dual Joint Classifier (DJC), which integrates the strengths of RoBERTa and BERT architectures. The DJC model leverages Bidirectional Gated Recurrent Units (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) layers to capture complex sequential dependencies and nuanced sentiment expressions. Advanced training techniques such as Focal Loss and Hard Sample Mining address class imbalance and improve model robustness. To further validate the DJC model’s robustness, the larger TweetEval Sentiment dataset was also included, on which DJC outperformed conventional models despite increased training time. Evaluations were conducted on the Twitter US Airlines and Apple Twitter Sentiment datasets to verify experiments. The DJC model achieved 87.22% and 93.87% accuracies, respectively, and demonstrated improvement over other models like RoBERTa-GLG, BiLSTM(P), and SVM. These results highlight the DJC model’s effectiveness in handling diverse sentiment analysis tasks and its potential for real-world applications.