Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel
Han Pu1,2(),Zhang Wei1,Zhang Zhanpeng1,Wang Yuxin1,Fang Haoyu1
1School of Management, Nanjing University of Posts & Telecommunications, Nanjing 210003, China 2Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
[Objective] This paper proposes a multi-channel MCMF-A model for Weibo posts based on feature fusion and attention mechanism, aiming to further explore the semantic information of public health emergency. [Methods] Firstly, we generated word vectors with Word2vec and FastText at the feature vector embedding level, which were merged with the vectors of part-of-speech features and position features. Secondly, we constructed multi-channel layer based on CNN and BiLSTM to extract local and global features of Weibo posts. Thirdly, we utilized the attention mechanism to extract important features of the texts. Finally, we merged the multi-channel output results, and used the softmax function for sentiment classification. [Results] We examined MCMF-A model with 42 384 Weibo posts on COVID-19. The F1 value of the proposed model reached 90.21%, which was 9.71% and 9.14% higher than the benchmark CNN and BiLSTM models. [Limitations] More research is needed to expand the experiment data size to include more small and multi-modal information such as images and voices. [Conclusions] The proposed model could effectively conduct sentiment analysis with Weibo posts.
韩普, 张伟, 张展鹏, 王宇欣, 方浩宇. 基于特征融合和多通道的突发公共卫生事件微博情感分析*[J]. 数据分析与知识发现, 2021, 5(11): 68-79.
Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel. Data Analysis and Knowledge Discovery, 2021, 5(11): 68-79.
Max Length of Sentences Size of Word Vector Size of Sentiment Feature Vector Size of Position Feature Vector Batch Size Window Size Number of Feature Map Hidden Size of BiLSTM epochs
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