Beyond Verbal Cues: Emotional Contagion Graph Network for Causal Emotion Entailment
Abstract: Emotions are fundamental to conversational understanding. While significant advancements have been achieved in conversational emotion recognition and emotional response generation, recognizing the causes of eliciting emotions is less explored. Previous studies have primarily focused on identifying the causes of emotions by understanding verbal contextual utterances, overlooking that non-verbal emotional cues can elicit emotions. To address this issue, we develop an Emotional Contagion Graph Network (ECGN) that simulates the impact of non-verbal implicit emotions on the counterpart’s emotions. To achieve this, we construct a heterogeneous graph that simulates the transmission of non-verbal emotions alongside verbal influences. By applying message passing between nodes, the constructed graph effectively models both the implicit emotional dynamics and explicit verbal interactions. We evaluate ECGN ’s performance through extensive experiments on the benchmark dataset and compare it against multiple state-of-the-art models. Experimental results demonstrate the effectiveness of the proposed model.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: emotion detection and analysis, emoji prediction and analysis
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 7937
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