Expansion quantization network: A micro-emotion detection and annotation framework
Jingyi Zhou, Senlin Luo, Haofan Chen, Alemayehu Getahun Kumela, Alemayehu Getahun Kumela, Alemayehu Getahun Kumela, Alemayehu Getahun Kumela
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.
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…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Face and Expression Recognition