Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine

Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine

sentiment analysis;hotel; clustering; naïve bayes; 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 of the platforms used for giving and searching reviewsThis research usesdata 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.

Bayu Yanuargi1*, Ema Utami2, Kusrini3,Arli Aditya Parikesit4

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6139/998

Department of Magister of Informatics Engineering, UniversitasAMIKOM Yogyakarta, Yogyakarta, Indonesia

29-12-2024

FAJAR BAGUS W

PDF

ENGLISH

TEXT