Mood-Based Music Discovery: A System for Generating Personalized Thai Music Playlists Using Emotion Analysis
This study enhances the music-listening experience and promotes Thai artists. It provides users easy access to Thai songs that match their current moods and situations, making their music journey more enjoyable. The system analyzes users’ emotions through text input, such as typing their current fee...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/8/2/37 |
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| author | Porawat Visutsak Jirayut Loungna Siraphat Sopromrat Chanwit Jantip Parunyu Soponkittikunchai Xiabi Liu |
| author_facet | Porawat Visutsak Jirayut Loungna Siraphat Sopromrat Chanwit Jantip Parunyu Soponkittikunchai Xiabi Liu |
| author_sort | Porawat Visutsak |
| collection | DOAJ |
| description | This study enhances the music-listening experience and promotes Thai artists. It provides users easy access to Thai songs that match their current moods and situations, making their music journey more enjoyable. The system analyzes users’ emotions through text input, such as typing their current feelings, and processes this information using machine learning to create a playlist that resonates with their feelings. This study focuses on building a tool that caters to the preferences of Thai music listeners and encourages the consumption of a wider variety of Thai songs beyond popular trends. This study develops a tool that successfully creates personalized playlists by analyzing the listener’s emotions. Phrase and keyword recognition detect the listener’s emotions, generating playlists tailored to their feelings, thus improving their music-listening satisfaction. The classifiers employed in this study achieved the following accuracies: random forest (0.94), XGBoost (0.89), decision tree (0.85), logistic regression (0.79), and support vector machine (SVM) (0.78). |
| format | Article |
| id | doaj-art-2e8a035e39704b299ebdde423dec3bfc |
| institution | DOAJ |
| issn | 2571-5577 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj-art-2e8a035e39704b299ebdde423dec3bfc2025-08-20T03:14:25ZengMDPI AGApplied System Innovation2571-55772025-03-01823710.3390/asi8020037Mood-Based Music Discovery: A System for Generating Personalized Thai Music Playlists Using Emotion AnalysisPorawat Visutsak0Jirayut Loungna1Siraphat Sopromrat2Chanwit Jantip3Parunyu Soponkittikunchai4Xiabi Liu5Department of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, ThailandDepartment of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, ThailandDepartment of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, ThailandDepartment of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, ThailandDepartment of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, ThailandSchool of Computer Science and Technology, Beijing Institute Technology, Beijing 100811, ChinaThis study enhances the music-listening experience and promotes Thai artists. It provides users easy access to Thai songs that match their current moods and situations, making their music journey more enjoyable. The system analyzes users’ emotions through text input, such as typing their current feelings, and processes this information using machine learning to create a playlist that resonates with their feelings. This study focuses on building a tool that caters to the preferences of Thai music listeners and encourages the consumption of a wider variety of Thai songs beyond popular trends. This study develops a tool that successfully creates personalized playlists by analyzing the listener’s emotions. Phrase and keyword recognition detect the listener’s emotions, generating playlists tailored to their feelings, thus improving their music-listening satisfaction. The classifiers employed in this study achieved the following accuracies: random forest (0.94), XGBoost (0.89), decision tree (0.85), logistic regression (0.79), and support vector machine (SVM) (0.78).https://www.mdpi.com/2571-5577/8/2/37music mood classificationpersonalized playlistsmusic recommendationThai songmachine learning |
| spellingShingle | Porawat Visutsak Jirayut Loungna Siraphat Sopromrat Chanwit Jantip Parunyu Soponkittikunchai Xiabi Liu Mood-Based Music Discovery: A System for Generating Personalized Thai Music Playlists Using Emotion Analysis Applied System Innovation music mood classification personalized playlists music recommendation Thai song machine learning |
| title | Mood-Based Music Discovery: A System for Generating Personalized Thai Music Playlists Using Emotion Analysis |
| title_full | Mood-Based Music Discovery: A System for Generating Personalized Thai Music Playlists Using Emotion Analysis |
| title_fullStr | Mood-Based Music Discovery: A System for Generating Personalized Thai Music Playlists Using Emotion Analysis |
| title_full_unstemmed | Mood-Based Music Discovery: A System for Generating Personalized Thai Music Playlists Using Emotion Analysis |
| title_short | Mood-Based Music Discovery: A System for Generating Personalized Thai Music Playlists Using Emotion Analysis |
| title_sort | mood based music discovery a system for generating personalized thai music playlists using emotion analysis |
| topic | music mood classification personalized playlists music recommendation Thai song machine learning |
| url | https://www.mdpi.com/2571-5577/8/2/37 |
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