The Super Emotion Dataset

TL;DR

The Super Emotion Dataset provides a large, standardized resource based on a validated emotion taxonomy, facilitating more consistent and comprehensive emotion recognition research across various NLP domains.

Contribution

It introduces a harmonized, large-scale emotion dataset grounded in psychological theory, addressing inconsistencies in existing resources.

Findings

Enables cross-domain emotion recognition research

Provides a standardized, large-scale dataset

Based on validated psychological emotion taxonomy

Abstract

Despite the wide-scale usage and development of emotion classification datasets in NLP, the field lacks a standardized, large-scale resource that follows a psychologically grounded taxonomy. Existing datasets either use inconsistent emotion categories, suffer from limited sample size, or focus on specific domains. The Super Emotion Dataset addresses this gap by harmonizing diverse text sources into a unified framework based on Shaver's empirically validated emotion taxonomy, enabling more consistent cross-domain emotion recognition research.