Multi-views Emotional Knowledge Extraction for Emotion Recognition in Conversation
1
Issued Date
2025-07-08
Resource Type
ISSN
09507051
Scopus ID
2-s2.0-105005491351
Journal Title
Knowledge-Based Systems
Volume
322
Rights Holder(s)
SCOPUS
Bibliographic Citation
Knowledge-Based Systems Vol.322 (2025)
Suggested Citation
Jian Z., Wu D., Wang S., He J., Yao J., Liu K., Wu Q. Multi-views Emotional Knowledge Extraction for Emotion Recognition in Conversation. Knowledge-Based Systems Vol.322 (2025). doi:10.1016/j.knosys.2025.113601 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/110377
Title
Multi-views Emotional Knowledge Extraction for Emotion Recognition in Conversation
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Emotion Recognition in Conversation (ERC) is a challenging task due to the scarcity and dispersion of contextual information across utterances. Most existing methods attempt to integrate comprehensive information to enhance utterance semantics, which, however, also introduces noise and irrelevant content, misleading the model and limiting its potential in emotion recognition. To this end, we introduce the concept of Conversational Clique (ConvClique) and propose CC-ERC, a multi-view emotional knowledge extraction method designed to capture the most relevant emotional cues within the ConvClique from complementary perspectives and collaboratively predict utterance emotions. Specifically, CC-ERC comprises two modules: 1) the Utterance Spatial Relationship (USR) module, which predicts emotions by modeling structural correlations among utterances, and 2) the Emotion Temporal Relationship (ETR) module, which captures emotion sequence patterns to determine utterance emotions. These modules are integrated to obtain the final prediction, contributing to the robustness and accuracy of emotion recognition. The effectiveness of CC-ERC is validated on three widely used ERC datasets, evaluated in both online and offline settings. Compared to the state-of-the-art methods, CC-ERC achieves average improvements of 0.63% in accuracy and 0.94% in weighted F1 scores. Ablation studies further validate the significance of ConvClique-based knowledge extraction and demonstrate the effectiveness of the USR and ETR modules in modeling utterance structural correlations and emotion sequence patterns.