Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

EMO-KNOW: A Large Scale Dataset on Emotion and Emotion-cause

Mia Huong Nguyen, Yasith Samaradivakara, Prasanth Sasikumar, Chitralekha Gupta, Suranga Nanayakkara

TL;DR

EMO-KNOW addresses the scarcity of large-scale emotion-cause data by introducing a dataset built from 9.8 million tweets spanning 15 years and annotated with 48 emotion classes and abstractive causes. It employs a two-stage pipeline—data curation with pattern-based extraction and LLM-assisted abstractive labeling—to yield a high-quality, ~700k-entry dataset suitable for emotion-aware reasoning and knowledge-graph construction. The labeling quality is validated via human evaluation and comparisons across multiple LLMs, with T5-flan achieving strong automatic scores close to InstructGPT-derived labels. The resource is released open-source to enable downstream applications in empathetic dialogue, mental health support, and nuanced event-centered emotion analysis.

Abstract

Emotion-Cause analysis has attracted the attention of researchers in recent years. However, most existing datasets are limited in size and number of emotion categories. They often focus on extracting parts of the document that contain the emotion cause and fail to provide more abstractive, generalizable root cause. To bridge this gap, we introduce a large-scale dataset of emotion causes, derived from 9.8 million cleaned tweets over 15 years. We describe our curation process, which includes a comprehensive pipeline for data gathering, cleaning, labeling, and validation, ensuring the dataset's reliability and richness. We extract emotion labels and provide abstractive summarization of the events causing emotions. The final dataset comprises over 700,000 tweets with corresponding emotion-cause pairs spanning 48 emotion classes, validated by human evaluators. The novelty of our dataset stems from its broad spectrum of emotion classes and the abstractive emotion cause that facilitates the development of an emotion-cause knowledge graph for nuanced reasoning. Our dataset will enable the design of emotion-aware systems that account for the diverse emotional responses of different people for the same event.

EMO-KNOW: A Large Scale Dataset on Emotion and Emotion-cause

TL;DR

EMO-KNOW addresses the scarcity of large-scale emotion-cause data by introducing a dataset built from 9.8 million tweets spanning 15 years and annotated with 48 emotion classes and abstractive causes. It employs a two-stage pipeline—data curation with pattern-based extraction and LLM-assisted abstractive labeling—to yield a high-quality, ~700k-entry dataset suitable for emotion-aware reasoning and knowledge-graph construction. The labeling quality is validated via human evaluation and comparisons across multiple LLMs, with T5-flan achieving strong automatic scores close to InstructGPT-derived labels. The resource is released open-source to enable downstream applications in empathetic dialogue, mental health support, and nuanced event-centered emotion analysis.

Abstract

Emotion-Cause analysis has attracted the attention of researchers in recent years. However, most existing datasets are limited in size and number of emotion categories. They often focus on extracting parts of the document that contain the emotion cause and fail to provide more abstractive, generalizable root cause. To bridge this gap, we introduce a large-scale dataset of emotion causes, derived from 9.8 million cleaned tweets over 15 years. We describe our curation process, which includes a comprehensive pipeline for data gathering, cleaning, labeling, and validation, ensuring the dataset's reliability and richness. We extract emotion labels and provide abstractive summarization of the events causing emotions. The final dataset comprises over 700,000 tweets with corresponding emotion-cause pairs spanning 48 emotion classes, validated by human evaluators. The novelty of our dataset stems from its broad spectrum of emotion classes and the abstractive emotion cause that facilitates the development of an emotion-cause knowledge graph for nuanced reasoning. Our dataset will enable the design of emotion-aware systems that account for the diverse emotional responses of different people for the same event.
Paper Structure (18 sections, 5 figures, 5 tables)

This paper contains 18 sections, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methods
  4. Data Curation
  5. Data Labeling
  6. Emotion Labeling
  7. Labeling with Large Language Models
  8. Evalution
  9. Evaluation criteria
  10. Evaluation procedure
  11. Evaluation Results
  12. Conclusion
  13. Limitations
  14. Ethical considerations
  15. Acknowledgements
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Examples of data points in EMO-KNOW
  • Figure 2: Data generation pipeline
  • Figure 3: Human Evaluator Questions
  • Figure 4: Human Evaluator Questions
  • Figure 5: Human Evaluator Questions