EmoGraph: Capturing Emotion Correlations using Graph Networks
This addresses the problem of ignoring emotion correlations in recognition systems for applications like affective computing, but it is incremental as it builds on existing graph network methods.
The paper tackles emotion recognition by capturing interconnections among emotions using graph networks based on co-occurrence statistics, and shows that EmoGraph outperforms baselines on multi-label classification datasets, particularly in macro-F1, and benefits single-label classification tasks.
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.