Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review
Reviews are a person's way of expressing feedback on something in the form of criticism and ideas. Reviews of mobile apps are a type of user feedback that focuses on the performance and look of a mobile application and is typically featured on the download page of a mobile application, such as...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923006662 |
| _version_ | 1797660426142482432 |
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| author | Riccosan Karen Etania Saputra |
| author_facet | Riccosan Karen Etania Saputra |
| author_sort | Riccosan |
| collection | DOAJ |
| description | Reviews are a person's way of expressing feedback on something in the form of criticism and ideas. Reviews of mobile apps are a type of user feedback that focuses on the performance and look of a mobile application and is typically featured on the download page of a mobile application, such as in the Apps Store. Because it comprises a person's feelings and emotions, whether they are joyful, sad, hostile, or indifferent toward a mobile application, the review data is textual and may be gathered and utilized as material for creating a textual dataset. This work creates a multi-label multi-class Indonesian-language dataset based on public reviews of mobile applications with sentiment and emotional values. Another factor supporting the creation of this dataset is the fact that there is still a limited number of textual datasets based on the Indonesian language that are multi-label multiclass for performing sentiment analysis tasks, particularly those linked to text classification tasks. The data generated by this research was cleaned and handled during the pre-processing step and was annotated with 3 sentiments, namely positive, negative, and neutral, as well as 6 emotions, namely anger, fear, sad, happy, love, and neutral. |
| first_indexed | 2024-03-11T18:30:39Z |
| format | Article |
| id | doaj.art-1de770318dfd4fd086d9d5810edd00fe |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-11T18:30:39Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-1de770318dfd4fd086d9d5810edd00fe2023-10-13T11:05:05ZengElsevierData in Brief2352-34092023-10-0150109576Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review Riccosan0Karen Etania Saputra1Corresponding author.; Computer Science Department, School of Computer Science, Bina Nusantara University Bandung Campus, Jakarta, Indonesia 11480Computer Science Department, School of Computer Science, Bina Nusantara University Bandung Campus, Jakarta, Indonesia 11480Reviews are a person's way of expressing feedback on something in the form of criticism and ideas. Reviews of mobile apps are a type of user feedback that focuses on the performance and look of a mobile application and is typically featured on the download page of a mobile application, such as in the Apps Store. Because it comprises a person's feelings and emotions, whether they are joyful, sad, hostile, or indifferent toward a mobile application, the review data is textual and may be gathered and utilized as material for creating a textual dataset. This work creates a multi-label multi-class Indonesian-language dataset based on public reviews of mobile applications with sentiment and emotional values. Another factor supporting the creation of this dataset is the fact that there is still a limited number of textual datasets based on the Indonesian language that are multi-label multiclass for performing sentiment analysis tasks, particularly those linked to text classification tasks. The data generated by this research was cleaned and handled during the pre-processing step and was annotated with 3 sentiments, namely positive, negative, and neutral, as well as 6 emotions, namely anger, fear, sad, happy, love, and neutral.http://www.sciencedirect.com/science/article/pii/S2352340923006662SentimentEmotionText classificationDatasetMultilabelMulticlass |
| spellingShingle | Riccosan Karen Etania Saputra Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review Data in Brief Sentiment Emotion Text classification Dataset Multilabel Multiclass |
| title | Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review |
| title_full | Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review |
| title_fullStr | Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review |
| title_full_unstemmed | Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review |
| title_short | Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review |
| title_sort | multilabel multiclass sentiment and emotion dataset from indonesian mobile application review |
| topic | Sentiment Emotion Text classification Dataset Multilabel Multiclass |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923006662 |
| work_keys_str_mv | AT riccosan multilabelmulticlasssentimentandemotiondatasetfromindonesianmobileapplicationreview AT karenetaniasaputra multilabelmulticlasssentimentandemotiondatasetfromindonesianmobileapplicationreview |