Decoding Sentiments: Enhancing COVID-19 Tweet Analysis through BERT-RCNN Fusion
DOI:
https://doi.org/10.53469/jtpes.2024.04(01).12Keywords:
COVID-19, Sentiment Analysis, BERT-RCNNAbstract
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.
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Copyright (c) 2024 Jize Xiong, Mingyang Feng, Xiaosong Wang, Chufeng Jiang, Ning Zhang, Zhiming Zhao
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