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An Emotion Analysis Dataset of Course Comment Texts in Massive Online Learning Course Platforms

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Title: An Emotion Analysis Dataset of Course Comment Texts in Massive Online Learning Course Platforms
Language: English
Authors: Xiang Feng (ORCID 0000-0003-2251-2587), Keyi Yuan (ORCID 0000-0003-0648-7940), Xiu Guan (ORCID 0000-0003-0566-5135), Longhui Qiu
Source: Interactive Learning Environments. 2024 32(4):1219-1233.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 15
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: MOOCs, Psychological Patterns, Artificial Intelligence, Prediction, Accuracy, Data Analysis, Foreign Countries
Geographic Terms: China
DOI: 10.1080/10494820.2022.2115517
ISSN: 1049-4820
1744-5191
Abstract: Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning platforms using the "three-person voting label method" based on the "sentence-level" and multi-category labeling dimensions with our self-developed system. After testing the consistency of the labeling results using the "Fleiss Kappa" method, we found that the consistency of the dataset was about 0.51, representing a moderate strength of agreement. Based on the dataset, the prediction accuracy of the Long-Short Term Memory (LSTM) method is about 0.68. This dataset provides a benchmark for the multi-category emotion dataset in the Chinese online learning field. It can provide a basis for the subsequent solution of emotion analysis, monitoring, and intervention in the education field. It can also provide a reference for constructing subsequent datasets in the education field.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1429173
Database: ERIC
FullText Text:
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  Data: An Emotion Analysis Dataset of Course Comment Texts in Massive Online Learning Course Platforms
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  Data: <searchLink fieldCode="AR" term="%22Xiang+Feng%22">Xiang Feng</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2251-2587">0000-0003-2251-2587</externalLink>)<br /><searchLink fieldCode="AR" term="%22Keyi+Yuan%22">Keyi Yuan</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0648-7940">0000-0003-0648-7940</externalLink>)<br /><searchLink fieldCode="AR" term="%22Xiu+Guan%22">Xiu Guan</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0566-5135">0000-0003-0566-5135</externalLink>)<br /><searchLink fieldCode="AR" term="%22Longhui+Qiu%22">Longhui Qiu</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Interactive+Learning+Environments%22"><i>Interactive Learning Environments</i></searchLink>. 2024 32(4):1219-1233.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: Y
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  Data: 15
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  Label: Abstract
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  Data: Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning platforms using the "three-person voting label method" based on the "sentence-level" and multi-category labeling dimensions with our self-developed system. After testing the consistency of the labeling results using the "Fleiss Kappa" method, we found that the consistency of the dataset was about 0.51, representing a moderate strength of agreement. Based on the dataset, the prediction accuracy of the Long-Short Term Memory (LSTM) method is about 0.68. This dataset provides a benchmark for the multi-category emotion dataset in the Chinese online learning field. It can provide a basis for the subsequent solution of emotion analysis, monitoring, and intervention in the education field. It can also provide a reference for constructing subsequent datasets in the education field.
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      – Text: English
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        PageCount: 15
        StartPage: 1219
    Subjects:
      – SubjectFull: MOOCs
        Type: general
      – SubjectFull: Psychological Patterns
        Type: general
      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Accuracy
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      – SubjectFull: Data Analysis
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      – SubjectFull: Foreign Countries
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      – SubjectFull: China
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      – TitleFull: An Emotion Analysis Dataset of Course Comment Texts in Massive Online Learning Course Platforms
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            NameFull: Xiang Feng
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            NameFull: Keyi Yuan
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            NameFull: Xiu Guan
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            NameFull: Longhui Qiu
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