Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Eojin Kang, Yunseok Choi and Juae Kim ()
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Eojin Kang: Department of German, Hankuk University of Foreign Studies, Seoul 02450, Republic of Korea
Yunseok Choi: Division of Language & AI, Hankuk University of Foreign Studies, Seoul 02450, Republic of Korea
Juae Kim: Department of English Linguistics and Language Technology, Division of Language & AI, Hankuk University of Foreign Studies, Seoul 02450, Republic of Korea

Mathematics, 2024, vol. 12, issue 11, 1-20

Abstract: Recently, the analysis of emotions in social media has been considered a significant NLP task in digital and social-media-driven environments due to their pervasive influence on communication, culture, and consumer behavior. In particular, the task of Aspect-Based Emotion Analysis (ABEA), which involves analyzing the emotions of various targets within a single sentence, has drawn attention to understanding complex and sophisticated human language. However, ABEA is a challenging task in languages with limited data and complex linguistic properties, such as Korean, which follows spiral thought patterns and has agglutinative characteristics. Therefore, we propose a Korean Target-Attention-Based Emotion Classifier (KOTAC) designed to utilize target information by unveiling emotions buried within intricate Korean language patterns. In the experiment section, we compare various methods of utilizing and representing vectors of target information for the attention mechanism. Specifically, our final model, KOTAC, shows a performance enhancement on the MTME (Multiple Targets Multiple Emotions) samples, which include multiple targets and distinct emotions within a single sentence, achieving a 0.72% increase in F1 score over a baseline model without effective target utilization. This research contributes to the development of Korean language models that better reflect syntactic features by innovating methods to not only obtain but also utilize target-focused representations.

Keywords: emotion classification; ABSA; ABEA; attention mechanism; Korean language characteristics; social media (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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