Papers › EmoGraph: Capturing Emotion Correlations using Graph Networks

EmoGraph: Capturing Emotion Correlations using Graph Networks

21 Aug 2020arXiv:2008.09378archive 2025-07-28

Peng Xu, Zihan Liu, Genta Indra Winata, Zhaojiang Lin, Pascale Fung

Most emotion recognition methods tackle the emotion understanding task by considering individual emotion independently while ignoring their fuzziness nature and the interconnections among them. In this paper, we explore how emotion correlations can be captured and help different classification tasks. We propose EmoGraph that captures the dependencies among different emotions through graph networks. These graphs are constructed by leveraging the co-occurrence statistics among different emotion categories. Empirical results on two multi-label classification datasets demonstrate that EmoGraph outperforms strong baselines, especially for macro-F1. An additional experiment illustrates the captured emotion correlations can also benefit a single-label classification task.

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Tasks

Classification Emotion Classification Emotion Recognition General Classification MUlTI-LABEL-ClASSIFICATION Multi-Label Classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Emotion Classification	SemEval 2018 Task 1E-c	BERT-GCN	Accuracy	0.589	#3 of 4	Archive leaderboard	report
Emotion Classification	SemEval 2018 Task 1E-c	BERT-GCN	Macro-F1	0.563	#3 of 4	Archive leaderboard	report
Emotion Classification	SemEval 2018 Task 1E-c	BERT-GCN	Micro-F1	0.707	#3 of 4	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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