Human-LLM Collaborative Construction of a Cantonese Emotion Lexicon

TL;DR

This paper presents a collaborative approach using both Large Language Models and human annotators to construct a Cantonese emotion lexicon, demonstrating improved quality and applicability for emotion extraction in a low-resource language.

Contribution

It introduces a novel human-LLM collaborative method for building emotion lexicons tailored to low-resource languages like Cantonese, integrating multiple linguistic resources.

Findings

The constructed lexicon effectively captures colloquial expressions.

Collaborative annotation enhances emotion label quality.

The lexicon improves emotion extraction accuracy.

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in language understanding and generation. Advanced utilization of the knowledge embedded in LLMs for automated annotation has consistently been explored. This study proposed to develop an emotion lexicon for Cantonese, a low-resource language, through collaborative efforts between LLM and human annotators. By integrating emotion labels provided by LLM and human annotators, the study leveraged existing linguistic resources including lexicons in other languages and local forums to construct a Cantonese emotion lexicon enriched with colloquial expressions. The consistency of the proposed emotion lexicon in emotion extraction was assessed through modification and utilization of three distinct emotion text datasets. This study not only validates the efficacy of the constructed lexicon but also emphasizes that collaborative annotation between human and artificial intelligence can significantly enhance the quality of emotion labels, highlighting the potential of such partnerships in facilitating natural language processing tasks for low-resource languages.