# EmoGraph: Capturing Emotion Correlations using Graph Networks

arXiv: https://arxiv.org/abs/2008.09378 · HTML: https://mlsanity.com/paper/2008.09378/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [Multimodal End-to-End Sparse Model for Emotion Recognition](https://mlsanity.com/paper/2103.09666/) (2021)
- [SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://mlsanity.com/paper/2101.10038/) (2021)
- [Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion](https://mlsanity.com/paper/2210.15842/) (2022)
- [An Effective Deployment of Contrastive Learning in Multi-label Text Classification](https://mlsanity.com/paper/2212.00552/) (2022)
- [Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats](https://mlsanity.com/paper/2211.00171/) (2022)
- [Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification](https://mlsanity.com/paper/2404.08720/) (2024)
- [LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification](https://mlsanity.com/paper/2103.14620/) (2021)
- [Multi-Label Contrastive Learning : A Comprehensive Study](https://mlsanity.com/paper/2412.00101/) (2024)
- [AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification](https://mlsanity.com/paper/2104.04517/) (2021)
- [Effective Transfer Learning for Low-Resource Natural Language Understanding](https://mlsanity.com/paper/2208.09180/) (2022)
- [Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function](https://mlsanity.com/paper/2410.03979/) (2024)

## Abstract (arXiv)

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.