Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine
Visitor reviews play a crucial role in determining the success of a business, particularly those offering hospitality and services, such as hotels. The growth of internet technology has made it easier for guests to share their experiences, which can influence potential customers. Google Maps is one...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6139 |
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| _version_ | 1841544035505799168 |
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| author | Bayu Yanuargi Ema Utami Kusrini Arli Aditya Parikesit |
| author_facet | Bayu Yanuargi Ema Utami Kusrini Arli Aditya Parikesit |
| author_sort | Bayu Yanuargi |
| collection | DOAJ |
| description | Visitor reviews play a crucial role in determining the success of a business, particularly those offering hospitality and services, such as hotels. The growth of internet technology has made it easier for guests to share their experiences, which can influence potential customers. Google Maps is one of the platforms used for giving and searching reviews This research uses data crawled from Google Maps Review using the playwright library. However, the large volume of reviews can make analysis and topic-based categorization—such as service quality, hotel location, and operational hours—challenging. To address this, DBSCAN is used to cluster reviews based on these topics. Clustering helps improve sentiment classification, making it more targeted and allowing a comparison of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Naïve Bayes achieved higher accuracy (0.87) in the operational hours cluster, while SVM scored 0.78. However, SVM showed improved accuracy in the location (0.89) and service (0.88) clusters, with Naïve Bayes maintaining a stable 0.86 accuracy in both. Both models demonstrated an average training time of less than one second, excluding preprocessing. |
| format | Article |
| id | doaj-art-2031c6ffb8a447e0bbc9b7c491399439 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-2031c6ffb8a447e0bbc9b7c4913994392025-01-13T03:30:32ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-12-018681982710.29207/resti.v8i6.61396139Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector MachineBayu Yanuargi0Ema Utami1Kusrini2Arli Aditya Parikesit3Universitas Amikom YogyakartaUniversitas AMIKOM YogyakartaUniversitas AMIKOM YogyakartaIndonesia International Institute for Life SciencesVisitor reviews play a crucial role in determining the success of a business, particularly those offering hospitality and services, such as hotels. The growth of internet technology has made it easier for guests to share their experiences, which can influence potential customers. Google Maps is one of the platforms used for giving and searching reviews This research uses data crawled from Google Maps Review using the playwright library. However, the large volume of reviews can make analysis and topic-based categorization—such as service quality, hotel location, and operational hours—challenging. To address this, DBSCAN is used to cluster reviews based on these topics. Clustering helps improve sentiment classification, making it more targeted and allowing a comparison of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Naïve Bayes achieved higher accuracy (0.87) in the operational hours cluster, while SVM scored 0.78. However, SVM showed improved accuracy in the location (0.89) and service (0.88) clusters, with Naïve Bayes maintaining a stable 0.86 accuracy in both. Both models demonstrated an average training time of less than one second, excluding preprocessing.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6139sentiment analysishotelclusteringnaïve bayessupport vector machine |
| spellingShingle | Bayu Yanuargi Ema Utami Kusrini Arli Aditya Parikesit Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) sentiment analysis hotel clustering naïve bayes support vector machine |
| title | Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine |
| title_full | Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine |
| title_fullStr | Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine |
| title_full_unstemmed | Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine |
| title_short | Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine |
| title_sort | data clustering for sentiment classification with naive bayes and support vector machine |
| topic | sentiment analysis hotel clustering naïve bayes support vector machine |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6139 |
| work_keys_str_mv | AT bayuyanuargi dataclusteringforsentimentclassificationwithnaivebayesandsupportvectormachine AT emautami dataclusteringforsentimentclassificationwithnaivebayesandsupportvectormachine AT kusrini dataclusteringforsentimentclassificationwithnaivebayesandsupportvectormachine AT arliadityaparikesit dataclusteringforsentimentclassificationwithnaivebayesandsupportvectormachine |