1School of Information Management, Nanjing University, Nanjing 210023, China 2School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China 3Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
[Objective] This study conducts sentiment analysis using the emotional knowledge contained in the syntactic structures of texts from online health communities. We propose an online health community sentiment analysis model, WoBEK-GAT, based on emotional enhancement and knowledge fusion. [Methods] Firstly, we utilized WoBERT Plus for dynamic word embedding. Then, we extracted semantic features using CNN and BiLSTM. Finally, we fully integrated key syntactic information from pruned dependency trees with external emotional knowledge through sentiment enhancement and knowledge fusion strategies. We fed these inputs into the GAT to output sentiment categories. [Results] We conducted comparative experiments on a constructed Chinese dataset. The proposed model’s MacroF1 value reached 88.48%. It was 15.49%, 14.15%, and 13.15% over baseline models CNN, BiLSTM, and GAT, respectively. [Limitations] We should have considered sentiment knowledge in multimodal information such as pictures and speeches. [Conclusions] The proposed model could effectively improve sentiment analysis capability.
张伟, 徐宗煌, 蔡鸿宇, 韩普, 石进. 基于情感增强和知识融合的在线健康社区情感分析研究*[J]. 数据分析与知识发现, 2024, 8(3): 53-62.
Zhang Wei, Xu Zonghuang, Cai Hongyu, Han Pu, Shi Jin. Sentiment Analysis of Online Health Community Based on Emotional Enhancement and Knowledge Fusion. Data Analysis and Knowledge Discovery, 2024, 8(3): 53-62.
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