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http://dx.doi.org/10.5909/JBE.2022.27.6.940

A Study on the Dataset of the Korean Multi-class Emotion Analysis in Radio Listeners' Messages  

Jaeah, Lee (Graduate School of Nano IT Design Fusion, Seoul National University of Science and Technology)
Gooman, Park (Graduate School of Nano IT Design Fusion, Seoul National University of Science and Technology)
Publication Information
Journal of Broadcast Engineering / v.27, no.6, 2022 , pp. 940-943 More about this Journal
Abstract
This study aims to analyze the Korean dataset by performing Korean sentence Emotion Analysis in the radio listeners' text messages collected personally. Currently, in Korea, research on the Emotion Analysis of Korean sentences is variously continuing. However, it is difficult to expect high accuracy of Emotion Analysis due to the linguistic characteristics of Korean. In addition, a lot of research has been done on Binary Sentiment Analysis that allows positive/negative classification only, but Multi-class Emotion Analysis that is classified into three or more emotions requires more research. In this regard, it is necessary to consider and analyze the Korean dataset to increase the accuracy of Multi-class Emotion Analysis for Korean. In this paper, we analyzed why Korean Emotion Analysis is difficult in the process of conducting Emotion Analysis through surveys and experiments, proposed a method for creating a dataset that can improve accuracy and can be used as a basis for Emotion Analysis of Korean sentences.
Keywords
Emotion Analysis; Natural Language Processing; Dataset; Multi-class; Korean language model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Kim.Jihee, Oh.Jinhee, Kim.Myeungjin, Lim,Yankyu, "A Study on the Method of Creating Realistic Content in Audience-participating Performances using Artificial Intelligence Sentiment Analysis Technology", The Korean Society of Broadcast and Media Engineers, Vol.26, No.5, pp.533-541, 2021. doi: http://doi.org/10.5909/JBE.2021.26.5.533   DOI
2 Sudharsan Ravichandiran, Getting Started with Google BERT, (H. Jeon, S. Jung, H. Kim, Trans.), Hanbit Media, pp.22-74, 341-344, 2021
3 Aurelien Geron, Hands-on Machin Learning with Scikit-Learn,Keras, and TensorFlow:Concepts,Tools,and Techniques to Build Intelligent Systems, (H. Park,Trans.), O'reily Media, , pp.598-670, 2020.
4 Kwang-Hyeon Pak, Seung-Hoon Na, Jong-Hoon Shin, Young-Kil Kim, "BERT for Korean Natural Language Processing: Named Entity Tagging, Sentiment Analysis, Dependency Parsing and Semantic Role Labeling", Korea Computer Congress 2019, Korea, pp.584-586, 2019
5 Yeonji Jang, Jiseon Choi, Hansaem Kim, "KcBert-based Movie review Corpus Emotion Analysis Using Emotion Vocabulary Dictionary" Journal of KIISE, Vol.49, No.8, pp.608-616, 2022.8. doi: https://doi.org/10.5626/JOK.2022.49.8.608   DOI
6 Sangah Lee, Hansol Jang, Yunmee Baik, Suzi Park, Hyopil Shin,"A Small-Scale Korean-Specific BERT Language Model", Journal of KIISE, Vol. 47, No. 7, pp. 682-692, 2020. 7. doi: https://doi.org/10.5626/JOK.2020.47.7.682   DOI