A Semantic-Enhanced Heterogeneous Dialogue Graph Network for Sentiment Analysis in Conversations
Jiating Zhao, Weijun Gao · 2024
The dynamics and complexity of emotional expressions in multi-party dialogues are frequently overlooked by traditional approaches. These expressions are characterised by participants' predisposition towards indirect emotional language, contextual dependency of emotions, and changes in emotional state with topic transitions. A semantic-enhanced heterogeneous dialogue graph model is presented to effectively capture emotional expressions in multi-party talks in order to overcome these issues. In order to extract implicit information from utterances, the model incorporates external emotional knowledge and uses graph neural networks to reconstruct adjacency matrices. Additionally, the impact of topic information on emotions is taken into consideration by using an improved variational autoencoder. A topic segmentation technique is used to create dialogue subgraphs for emotion categorization, together with semantic and topic information. Experimental results on the IEMOCAP, MELD, and EmoryNLP datasets show that the proposed model outperforms the state-of-the-art baseline models in terms of classification accuracy, greatly improving the performance of multi-party discourse emotion analysis.