# SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats

arXiv: https://arxiv.org/abs/1909.07734 · HTML: https://mlsanity.com/paper/1909.07734/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [Fine-Grained Emotion Prediction by Modeling Emotion Definitions](https://mlsanity.com/paper/2107.12135/) (2021)
- [Uncovering the Limits of Text-based Emotion Detection](https://mlsanity.com/paper/2109.01900/) (2021)
- [MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations](https://mlsanity.com/paper/2102.01263/) (2021)
- [Emotions in the Loop: A Survey of Affective Computing for Emotional Support](https://mlsanity.com/paper/2505.01542/) (2025)
- [Advancing Multi-Party Dialogue Framework with Speaker-ware Contrastive Learning](https://mlsanity.com/paper/2501.11292/) (2025)
- [Enhancing Sentiment Analysis Results through Outlier Detection Optimization](https://mlsanity.com/paper/2311.16185/) (2023)
- [Can MLLMs Generalize to Multi-Party dialog? Exploring Multilingual Response Generation in Complex Scenarios](https://mlsanity.com/paper/2501.11269/) (2025)
- [Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement](https://mlsanity.com/paper/2601.13481/) (2026)
- [Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation](https://mlsanity.com/paper/2604.06784/) (2026)
- [Chiaroscuro for Emotions: A Contrastive Emotion Benchmark Grounded in Appraisal Theory](https://mlsanity.com/paper/2609.03394/) (2026)

## Abstract (arXiv)

We present an overview of the EmotionX 2019 Challenge, held at the 7th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with IJCAI 2019. The challenge entailed predicting emotions in spoken and chat-based dialogues using augmented EmotionLines datasets. EmotionLines contains two distinct datasets: the first includes excerpts from a US-based TV sitcom episode scripts (Friends) and the second contains online chats (EmotionPush). A total of thirty-six teams registered to participate in the challenge. Eleven of the teams successfully submitted their predictions performance evaluation. The top-scoring team achieved a micro-F1 score of 81.5% for the spoken-based dialogues (Friends) and 79.5% for the chat-based dialogues (EmotionPush).