The Super Emotion Dataset
This provides a resource for researchers in NLP and psychology to enable more consistent cross-domain emotion recognition, though it is incremental as it builds on existing taxonomies.
The authors tackled the lack of a standardized, large-scale emotion classification dataset in NLP by creating the Super Emotion Dataset, which harmonizes diverse text sources into a unified framework based on Shaver's empirically validated taxonomy.
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.