Multi-Aspect Sentiment Analysis Hotel Review Using RF, SVM, and Naïve Bayes based Hybrid Classifier

 I Putu Ananda Miarta Utama (Telkom University, Bandung, Indonesia)
 Sri Suryani Prasetyowati (Telkom University, Bandung, Indonesia)
 (*)Yuliant Sibaroni Mail (Telkom University, Bandung, Indonesia)

(*) Corresponding Author

Submitted: March 27, 2021; Published: April 25, 2021


In the hotel tourism sector, of course, it cannot be separated from the role of social media because tourists tend to share experiences about services and products offered by a hotel, such as adding pictures, reviews, and ratings which will be helpful as references for other tourists, for example on the media online TripAdvisor. However, tourists' many experiences regarding a hotel make some people feel confused in determining the right hotel to visit. Therefore, in this study, an aspect-based analysis of reviews on hotels is carried out, which will make it easier for tourists to determine the right hotel based on the best category aspects. The dataset used is the TripAdvisor Hotel Reviews dataset which is already on the Kaggle website. And has five aspects, namely Room, Location, Cleanliness, Registration, and Service. A review analysis was carried out into positive and negative categories using the Random Forest, SVM, and Naive Bayes based Hybrid Classifier methods to solve this problem. In this study the Hybrid Classifier method gets better accuracy than the classification using one algorithm on multi-aspect data, namely the Hybrid Classifier got an average accuracy 84%, Naïve Bayes got an average accuracy 82.4%, Random Forest got an average accuracy 82.2%, and use SVM got an average accuracy 81%


Hotel; Random Forest; SVM; Hybrid Classifier; Multi-aspect Sentiment Analysis; Naïve Bayes

Full Text:


Article Metrics

Abstract view : 897 times
PDF - 608 times


R. A. Priyantina and R. Sarno, “Sentiment analysis of hotel reviews using Latent Dirichlet Allocation, semantic similarity and LSTM,” Int. J. Intell. Eng. Syst., vol. 12, no. 4, pp. 142–155, 2019, doi: 10.22266/ijies2019.0831.14.

N. Akhtar, N. Zubair, A. Kumar, and T. Ahmad, “Aspect based Sentiment Oriented Summarization of Hotel Reviews,” Procedia Comput. Sci., vol. 115, no. May 2020, pp. 563–571, 2017, doi: 10.1016/j.procs.2017.09.115.

A. A. Farisi, Y. Sibaroni, and S. Al Faraby, “Sentiment analysis on hotel reviews using Multinomial Naïve Bayes classifier,” J. Phys. Conf. Ser., vol. 1192, no. 1, 2019, doi: 10.1088/1742-6596/1192/1/012024.

F. A. Bachtiar, W. Paulina, and A. N. Rusydi, “Text Mining for Aspect Based Sentiment Analysis on Customer Review : a Case Study in the Hotel Industry,” 5th Int. Work. Innov. Inf. Commun. Sci. Technol., no. March, 2020.

D. A. K. Khotimah and R. Sarno, “Sentiment analysis of hotel aspect using probabilistic latent semantic analysis, word embedding and LSTM,” Int. J. Intell. Eng. Syst., vol. 12, no. 4, pp. 275–290, 2019, doi: 10.22266/ijies2019.0831.26.

Y. Al Amrani, M. Lazaar, and K. E. El Kadirp, “Random forest and support vector machine based hybrid approach to sentiment analysis,” Procedia Comput. Sci., vol. 127, pp. 511–520, 2018, doi: 10.1016/j.procs.2018.01.150.

S. Sangam and S. Shinde, “Sentiment classification of social media reviews using an ensemble classifier,” Indones. J. Electr. Eng. Comput. Sci., vol. 16, no. 1, p. 355, 2019, doi: 10.11591/ijeecs.v16.i1.pp355-363.

M. Govindarajan, “Sentiment Analysis of Movie Reviews using Hybrid Method of Naive Bayes and Genetic Algorithm,” Int. J. Adv. Comput. Res., no. 4, pp. 2277–7970, 2013, [Online]. Available:

A. Sharma, A. Sharma, R. K. Singh, and M. D. Upadhayay, “Hybrid Classifier for Sentiment Analysis Using Effective Pipelining,” Int. Res. J. Eng. Technol., vol. 4, no. 8, pp. 2276–2281, 2017, [Online]. Available:

Z. Wu, W. Lin, Z. Zhang, A. Wen, and L. Lin, “An Ensemble Random Forest Algorithm for Insurance Big Data Analysis,” Proc. - 2017 IEEE Int. Conf. Comput. Sci. Eng. IEEE/IFIP Int. Conf. Embed. Ubiquitous Comput. CSE EUC 2017, vol. 1, pp. 531–536, 2017, doi: 10.1109/CSE-EUC.2017.99.

Y. Al Amrani, M. Lazaar, and K. E. El Kadiri, “A novel hybrid classification approach for sentiment analysis of text document,” Int. J. Electr. Comput. Eng., vol. 8, no. 6, pp. 4554–4567, 2018, doi: 10.11591/ijece.v8i6.pp4554-4567.

M. M and S. Mehla, “Sentiment Analysis of Movie Reviews using Machine Learning Classifiers,” Int. J. Comput. Appl., vol. 182, no. 50, pp. 25–28, 2019, doi: 10.5120/ijca2019918756.

S. L. Mahfiz and A. Romadhony, “Aspect-based Opinion Mining on Beauty Product Reviews,” 2020 3rd Int. Semin. Res. Inf. Technol. Intell. Syst. ISRITI 2020, pp. 488–493, 2020, doi: 10.1109/ISRITI51436.2020.9315350.

Y. Lin, X. Wang, and A. Zhou, “Opinion spam detection,” Opin. Anal. Online Rev., no. May, pp. 79–94, 2016, doi: 10.1142/9789813100459_0007.

J. Li, H. Yang, and C. Zong, “Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings,” Proc. 27th Int. Conf. Comput. Linguist., pp. 925–936, 2018, [Online]. Available:

F. F. Rahmawati and Y. Sibaroni, “Multi-Aspect Sentiment Analysis pada Destinasi Pariwisata Yogyakarta Menggunakan Support Vector Machine dan Particle Swarm Optimization sebagai Seleksi Fitur,” 2019.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Multi-Aspect Sentiment Analysis Hotel Review Using RF, SVM, and Naïve Bayes based Hybrid Classifier


  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.