Sentiment Analysis of Online Health Community Based on Emotional Enhancement and Knowledge Fusion
[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.
Online Health CommunitySentiment AnalysisEmotional EnhancementKnowledge FusionGAT
NETL
NSTL
万方数据
