科技情报研究2024，Vol.6Issue(2)：88-99,12.DOI:10.19809/j.cnki.kjqbyj.2024.02.008
基于语义增强的在线健康社区情感分析研究
Sentiment Analysis of Online Healthy Community Based on Semantic Enhancement
摘要
Abstract
[Purpose/significance]In order to make full use of the value of text dependent syntactic information and prior emotion knowledge in emotion analysis,a semantic enhanced online healthy community emotion analysis model was proposed.[Method/process]Firstly,feature vectors for pre-processed online health community data are generated by Word2Vec and BERT;then local and global information of online review text are extracted using TextCNN and BiLSTM respectively based on dual-channel idea;then sentiment knowledge and dependency grammar information are merged in graph attention networks for semantic enhancement;finally,dual-channel features are fused and perform online health community sentiment classification in fully connected layer.[Result/conclusion]The comparative experiments on 31718 online health community comments show that the accuracy of the BERT-TBGH model based on semantic enhancement reaches 90.77%,which is 10.57%and 7.79%higher than the classical models TextCNN and BiLSTM and is 1.85%and 1.00%higher after introducing sentiment knowledge and character-level dependency syntactic information.The proposed model based on semantic enhanced BERT-TBGH model can effectively improve the effect of online healthy community emotion analysis.The defect of this article is that the experiment was limited to the online health community dataset and was not further validated on larger datasets.关键词
情感分析/依存句法分析/图神经网络/语义增强/BERT-TBGH/在线健康社区Key words
sentiment analysis/dependency parsing/graph neural networks/semantic enhancement/BERT-TBGH/online health communities分类
社会科学引用本文复制引用
韩普,叶东宇..基于语义增强的在线健康社区情感分析研究[J].科技情报研究,2024,6(2):88-99,12.基金项目
国家社会科学基金项目"面向多模态医疗健康数据的知识组织模式研究"(编号:22BTQ096) （编号:22BTQ096）
江苏高校青蓝工程和南京邮电大学1311人才计划资助 （）
江苏省研究生科研创新计划资助(编号:KYCX22_0870). （编号:KYCX22_0870）
