A Korean emotion-factor dataset for extracting emotion and factors in Korean conversations
Abstract Humans express their emotions in various ways, such as through facial expressions and voices. In particular, emotions are directly expressed or indirectly implied in the text of utterance. Research on the technology to identify emotions included in human speech and generate utterances is be...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-45386-8 |
| _version_ | 1797647324861693952 |
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| author | SoYeop Yoo HaYoung Lee JeIn Song OkRan Jeong |
| author_facet | SoYeop Yoo HaYoung Lee JeIn Song OkRan Jeong |
| author_sort | SoYeop Yoo |
| collection | DOAJ |
| description | Abstract Humans express their emotions in various ways, such as through facial expressions and voices. In particular, emotions are directly expressed or indirectly implied in the text of utterance. Research on the technology to identify emotions included in human speech and generate utterances is being conducted in conversational artificial intelligence technology. Despite the importance of recognizing the factors of previously generated emotions to generate emotion-based utterances, most of the existing datasets only provide the classification of emotions in text and utterances. In addition, in the case of Korean datasets, the classification of emotions is not diverse, and it is mainly biased toward negative emotion classification. In this paper, we propose KEmoFact, a Korean emotion-factor dataset for extracting emotion and factors in Korean conversations. We also define two tasks for the KEmoFact dataset, EFE (Emotion Factor Extraction) and EFPE (Emotion-Factor Pair Extraction), and propose baseline models for the tasks. We contribute to the study of conversational artificial intelligence, especially in Korean, one of the low-resource languages, by proposing the KEmoFact dataset and suggesting baseline models for two tasks. |
| first_indexed | 2024-03-11T15:14:38Z |
| format | Article |
| id | doaj.art-4bc3144383c54c6d95dcf35dbdb76430 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-11T15:14:38Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4bc3144383c54c6d95dcf35dbdb764302023-10-29T12:22:13ZengNature PortfolioScientific Reports2045-23222023-10-0113111210.1038/s41598-023-45386-8A Korean emotion-factor dataset for extracting emotion and factors in Korean conversationsSoYeop Yoo0HaYoung Lee1JeIn Song2OkRan Jeong3School of Computing, Gachon UniversitySchool of Computing, Gachon UniversityScatter LabSchool of Computing, Gachon UniversityAbstract Humans express their emotions in various ways, such as through facial expressions and voices. In particular, emotions are directly expressed or indirectly implied in the text of utterance. Research on the technology to identify emotions included in human speech and generate utterances is being conducted in conversational artificial intelligence technology. Despite the importance of recognizing the factors of previously generated emotions to generate emotion-based utterances, most of the existing datasets only provide the classification of emotions in text and utterances. In addition, in the case of Korean datasets, the classification of emotions is not diverse, and it is mainly biased toward negative emotion classification. In this paper, we propose KEmoFact, a Korean emotion-factor dataset for extracting emotion and factors in Korean conversations. We also define two tasks for the KEmoFact dataset, EFE (Emotion Factor Extraction) and EFPE (Emotion-Factor Pair Extraction), and propose baseline models for the tasks. We contribute to the study of conversational artificial intelligence, especially in Korean, one of the low-resource languages, by proposing the KEmoFact dataset and suggesting baseline models for two tasks.https://doi.org/10.1038/s41598-023-45386-8 |
| spellingShingle | SoYeop Yoo HaYoung Lee JeIn Song OkRan Jeong A Korean emotion-factor dataset for extracting emotion and factors in Korean conversations Scientific Reports |
| title | A Korean emotion-factor dataset for extracting emotion and factors in Korean conversations |
| title_full | A Korean emotion-factor dataset for extracting emotion and factors in Korean conversations |
| title_fullStr | A Korean emotion-factor dataset for extracting emotion and factors in Korean conversations |
| title_full_unstemmed | A Korean emotion-factor dataset for extracting emotion and factors in Korean conversations |
| title_short | A Korean emotion-factor dataset for extracting emotion and factors in Korean conversations |
| title_sort | korean emotion factor dataset for extracting emotion and factors in korean conversations |
| url | https://doi.org/10.1038/s41598-023-45386-8 |
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