吉林大学学报（理学版）2026，Vol.64Issue(3)：568-580,13.DOI:10.13413/j.cnki.jdxblxb.2025031
基于层级知识增强和义原知识的中文隐式情感分析
Chinese Implicit Sentiment Analysis Based on Hierarchical Knowledge Enhancement and Sememe Knowledge
摘要
Abstract
Aiming at the problems that there were the lack of explicit sentiment clues,mixed sentiment features,polysemy features and context dependence features in implicit sentiment analysis,we proposed a Chinese implicit sentiment analysis method based on hierarchical knowledge enhancement and sememe knowledge.We first introduced a sentiment pre-training model based on bidirectional encoder representation technology of converters to enhance the ability of sentiment clue recognition.Then we handled mixed sentiment features through character-level information acquisition,region moving box learning,global information learning,and multi-pooling operations.At the same time,we combined sememe knowledge and density matrix,utilized the HowNet knowledge base to allevite polysemy issues,and integrated with bidirectional long short-term memory network features to tackle context dependence features.Experimental results show that the proposed method performs excellently in terms of effectiveness,superiority,and generalizability,providing a valuable technical path for Chinese implicit sentiment analysis and helping to improve sentiment understanding and decision-making support capabilities in scenarios such as social media and user reviews.关键词
隐式情感分析/外部知识/知识增强/SentiBERT模型/层级知识/义原知识Key words
implicit sentiment analysis/external knowledge/knowledge enhancement/SentiBERT model/hierarchical knowledge/sememe knowledge分类
信息技术与安全科学引用本文复制引用
王红斌,张煊赫,侯明辉..基于层级知识增强和义原知识的中文隐式情感分析[J].吉林大学学报（理学版）,2026,64(3):568-580,13.基金项目
国家自然科学基金(批准号:61966020)和云南省基础研究面上项目(批准号:202201AT070157). （批准号:61966020）
