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dc.contributor.author김창환-
dc.contributor.author전태희-
dc.date.accessioned2021-08-11T15:30:02Z-
dc.date.available2021-08-11T15:30:02Z-
dc.date.issued2021-07-
dc.identifier.citationVOL 48, NO 7-801-
dc.identifier.issn2383-6296-
dc.identifier.other57206-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/73584-
dc.description.abstractCompared with most studies on categorical sentiment analysis which aims to represent emotional states as a small set of emotion categories, there have been fewer studies on dimensional sentiment analysis which treats sentiment analysis as a regression problem because of the shortage of data. Recently, the National Information Society Agency (NIA) released open data, Multimodal Video Data, through their web site, AI Hub. Using this data, we experimented with dimensional sentiment analysis of Korean text. For this purpose, we used CNN which is one of the conventional deep learning models in NLP. We also verified that data balancing could improve the performance of models. The results show that the model trained on Multimodal Video Data performs well enough to show that the data should be useful for dimensional sentiment analysis of Korean text and that with data balancing the model can perform better in spite of their fewer training data.-
dc.publisher정보과학회논문지-
dc.subject차원적 감성 분석-
dc.subject감정 회귀-
dc.subject한국어 텍스트-
dc.subject데이터 분포-
dc.subject데이터 균형화-
dc.titleDimensional Sentiment Analysis of Korean Text using Data Balancing-
dc.typeArticle-
dc.relation.page790801-
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