Expansion quantization network: A micro-emotion detection and annotation framework.

ABSTRACT: Textemotion detection constitutes a crucial foundation for advancing artificial intelligence from basic comprehension to the exploration of emotional reasoning. Most existing emotion detection datasets rely on manual annotations, which are associated with high costs, substantial subjectivity, and severe label imbalances. This is particularly evident in the inadequate annotation of micro-emotions and the absence of emotional intensity representation, which fail to capture the rich emotions embedded in sentences and adversely affect the quality of downstream task completion. By proposing an all-labels and training-set label regression method, we map label values to energy intensity levels, thereby fully leveraging the learning capabilities of machine models and the interdependencies among la

SUBMITTER: Zhou J 

PROVIDER: S-EPMC12614796 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature