Supervised Model for Sentiment Analysis Based on Hotel Review Clusters using RapidMiner


  • Revin Novian Juliadi Widyatama University, Bandung, Indonesia
  • Yan Puspitarani Widyatama University, Bandung, Indonesia




Data Mining, Text Mining, Sentiment Analysis, Opinion Mining, Text Clustering, Naive Bayes, K-Means, Natural Language Processing, RapidMiner


Customer feedback in the modern era like today is mostly presented in the form of digital reviews, including customer feedback at an inn or hotel, customer feedback is very valuable data where from this data the management can find out, identify and analyze the customer experience and what they need. With customer feedback in the form of digital reviews, it will allow a lot of data that can be obtained by hotel management and will provide many benefits if the data is processed correctly. To take advantage of large  text review data, a combination of data mining and natural language processing techniques was chosen to process text in depth and efficiently.  Text mining in the form of creating an opinion mining model using the Naïve Bayes classification algorithm is applied to find information and measure the main sentiments expressed in the reviewed text dataset, then the application of K-Means text grouping aims to group texts and get information about the main topics discussed from the content of the review dataset text in each group . By applying the constructed sentiment analysis model, approximately 90.90% accuracy results were obtained in reading texts and measuring sentiments related to hotel customer feedback data.

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How to Cite

Juliadi, R. N., & Puspitarani, Y. (2022). Supervised Model for Sentiment Analysis Based on Hotel Review Clusters using RapidMiner. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(3), 1059-1066.