Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection Framework

TL;DR

The paper introduces the Expansion Quantization Network (EQN), a novel framework for automatic micro-emotion detection and annotation in text, leveraging energy-level scores to improve emotion recognition accuracy and address annotation challenges.

Contribution

It proposes the first framework to automatically annotate micro-emotions with energy-level scores, enhancing emotion detection and addressing label imbalance issues in NLP datasets.

Findings

EQN outperforms existing models in micro-emotion detection accuracy.

The framework effectively leverages label interdependencies and energy-level mapping.

Validated across multiple sentiment datasets with broad applicability.

Abstract

Text emotion 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 labels to uncover multiple emotions within samples. This led to the establishment of the Emotion Quantization Network (EQN) framework for micro-emotion detection and annotation. Using five commonly employed sentiment datasets, we conducted comparative experiments with various models, validating the broad applicability of our framework within NLP machine learning models. Based on the EQN framework, emotion detection and annotation are conducted on the GoEmotions dataset. A comprehensive comparison with the results from Google literature demonstrates that the EQN framework possesses a high capability for automatic detection and annotation of micro-emotions. The EQN framework is the first to achieve automatic micro-emotion annotation with energy-level scores, providing strong support for further emotion detection analysis and the quantitative research of emotion computing.