Research on Sentiment Analysis of Weibo Comments Based on BERT-BILSTM-Attention Deep Learning

Abstract

Sentiment analysis of COVID-19-related content on Weibo is of significant importance for studying public sentiment during the pandemic and economic recovery. Due to the lack of well-annotated Chinese Weibo COVID-19 data (such as the Weibo NCOV dataset), as well as the emotional complexity and ambiguity of Chinese Weibo texts, this paper proposes an innovative sentiment analysis model for Chinese Weibo COVID-19 data, namely BERT-BiLSTM-Attention. The model first encodes Weibo comment data using BERT to enhance the semantic feature representation of the text and improve its contextual understanding. Next, BiLSTM is used to enrich the contextual information of the Weibo text, helping to extract important and effective information from the text sequences. Finally, an Attention mechanism is employed to quickly capture the most relevant information. Experimental results show that the model is effective in sentiment analysis of Weibo COVID-19 data, achieving an accuracy of 88.2%. It can be concluded that the proposed model significantly improves the performance of Weibo text classification and demonstrates strong generalizability, making it suitable for sentiment analysis in various fields.