Abstract: Conversational aspect-based sentiment quadruple analysis(DiaASQ) is an emerging research direction in the field of ABSA(Aspect-Based Sentiment Analysis),which aims to identify and extract sentiment quadruples－namely,target,aspect,opinion,and sentiment polarity－from a given dialogue.Compared with traditional ABSA tasks on static texts,DiaASQ faces two major challenges:1)dialogue texts are often lengthy,with sentiment elements such as targets,aspects,and opinions scattered across multiple utterances,making it difficult to capture long-range dependencies;2)dialogue structures are more complex,typically involving multiple speakers and reply relationships,where information frequently spans sentences and speakers,leading to intricate interaction patterns.To address these challenges,this paper proposes MVLLF-GF,a model that integrates multi-view local language features with global contextual representations for dialogue-based sentiment quadruple extraction.Specifically,a multi-view linguistic knowledge encoder is employed to enhance token-level interactions from multiple perspectives,including syntactic dependency and semantic information,thereby learning rich local features.A global utterance encoder is then introduced to capture global features by modeling speaker identities and reply relationships at theutterance level.Furthermore,a multi-granula-rity fusion module is designed to deeply integrate features across different levels,enhancing the model’s contextual understanding.Finally,an end-to-end grid tagging mechanism is applied to decode sentiment quadruples.Experimental results on the public DiaASQ Chinese dataset(ZH) and English dataset(EN) demonstrate that the proposed method achieves Micro-F1 improvements of 9.13 percentage points and 6.50 percentage points,respectively,over the baseline model MVQPN,verifying its effectiveness.

Key words: Conversational aspect-based sentiment quadruple, Syntactic dependency relation, Attention mechanisms, Semantic information, Graph convolutional network