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

## TL;DR

This paper introduces a new framework for detecting and annotating micro-emotions in text using an energy-level-based approach, reducing reliance on manual annotations.

## Contribution

The EQN framework is the first to enable automatic micro-emotion annotation with energy-level scores.

## Key findings

- The EQN framework outperforms existing methods in micro-emotion detection and annotation.
- The framework demonstrates broad applicability across multiple NLP models and datasets.
- It provides a more nuanced representation of emotional intensity compared to traditional label-based approaches.

## 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 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 its 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.