A mutual implicit sentiment analysis model with bundle-aware contrastive learning

As a crucial task in natural language processing, sentiment analysis is widely applied in different fields. However, various implicit expressions interspersed in explicit expressions have become a stumbling block for the further improvement of sentiment analysis. Existing studies usually introduce external knowledge, such as knowledge graph, the context of implicit expressions, or external corpus, to enhance implicit sentiment detection. Different from them, the core idea of this paper is to form explicit-implicit bundles to ensure each batch has the two expressions, which does not rely on external resources. The proposed MB-CL is a Mutual learning model with Bundle-aware Contrastive Learning for the implicit sentiment analysis. Our MB-CL leverages mutual learning between contextualized and local-perceived feature extractors and adopts bundle-aware contrastive learning to align explicit and implicit expressions with the same sentiment in the embedding space while pushing apart expressions with different sentiments. The experiment results on the SMP2019 dataset show that MB-CL outperforms comparative methods in terms of F1 measure.