桂林电子科技大学学报2026，Vol.46Issue(4)：394-402,9.DOI:10.16725/j.1673-808X.2023206
基于跨语言知识增强和对比学习的隐式情感分析
Implicit sentiment analysis based on cross-lingual knowledge enhancement and contrastive learning
摘要
Abstract
Implicit sentiment analysis plays a crucial role in understanding latent emotional expressions embedded in textual content.However,most existing sentiment analysis methods primarily focus on explicit sentiment cues while neglecting implicit sentiment information commonly found in real-world user-generated texts.This challenge is particularly pronounced in Chinese texts,where rich semantic structures and rhetorical devices,such as metaphors and similes,frequently obscure underlying sentiment expressions.To address these issues,a novel implicit sentiment analysis framework,termed CKECL,is proposed.First,a pre-trained translation model is employed to generate cross-lingual knowledge for data augmentation.The generated cross-lingual information enriches se-mantic representations and improves the model's capability to capture implicit sentiment patterns.Second,a bidirectional long short-term memory(BiLSTM)network is adopted to model contextual dependencies from both forward and backward directions,thereby enhancing sentence-level semantic representation.Finally,information from sentences,is used to better capture contextual features within the sentences.Finally,contrastive learning is introduced to learn more discriminative sentence representations by integrating cross-lingual knowledge with contextual features.Experimental results on multiple benchmark datasets demonstrate that the pro-posed approach consistently outperforms competing baselines,confirming its effectiveness.关键词
隐式情感/情感分析/预训练模型/知识增强/双向长短时记忆网络/对比学习Key words
implicit sentiment analysis/sentiment analysis/pre-trained model/knowledge enhancement/bidirectional long short-term memory networks/contrastive learning分类
信息技术与安全科学引用本文复制引用
方万红,林煜明..基于跨语言知识增强和对比学习的隐式情感分析[J].桂林电子科技大学学报,2026,46(4):394-402,9.基金项目
国家自然科学基金(62062027) （62062027）
桂林电子科技大学研究生教育创新计划(2022YCXS093) （2022YCXS093）
