Decoding Sentiments: Enhancing COVID-19 Tweet Analysis through BERT-RCNN Fusion - Xiong, Jize; Feng, Mingyang; Wang, Xiaosong; Jiang, Chufeng; Zhang, Ning; Zhao, Zhiming (Journal of Theory and Practice of Engineering Science, 2024)
In the era of the COVID-19 pandemic, the surge in information sharing on social media, particularly Twitter, necessitates a nuanced understanding of sentiments. Conventional sentiment analysis methods face challenges in capturing the evolving discourse's contextual nuances. This study introduces a novel approach, employing BERT-RCNN for sentiment classification of COVID-19-related tweets. BERT's bidirectional contextual insights combined with RCNN's feature extraction enhance our model's accuracy. The labels 'Neutral,' 'Positive,' and 'Negative' provide a nuanced emotional analysis. Our methodology overcomes traditional limitations, offering a context-aware sentiment analysis. By leveraging BERT-RCNN, this research contributes to a deeper understanding of public sentiments during the pandemic, addressing evolving challenges in sentiment classification.