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

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 invo...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2024-06, Vol.12 (11), p.1637
Main Authors: Kang, Eojin, Choi, Yunseok, Kim, Juae
Format: Article
Language:English
Subjects:
ABEA
ABSA
Attention
attention mechanism
Classification
Coffee
Comparative analysis
emotion classification
Emotion regulation
Emotions
Korean language
Korean language characteristics
Language
Linguistics
Mathematical models
Sentences
Sentiment analysis
social media
Social networks
Vector spaces
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12111637