Topic Modeling LDA and SVM in Sentiment Analysis of Hotel Reviews

Erniyati Erniyati, Prihastuti Harsani, Mulyati Mulyati, Lutfi Dani Fahriza

Abstract


The number of visitor comment review data that enters the TripAdvisor and Agoda sites continues to grow over time, this makes it difficult for the hotel to obtain overall information from all comment reviews. Therefore, the purpose of this study is to apply topic modeling and classifying in the analysis of hotel service sentiment.  The data for comment reviews were obtained from 3 five-star hotels, namely 1-HTL, 2-HTL and 3-HTL. The hotel has a five-star rating and has the most comments compared to other hotels in Jakarta. The topic modeling method using Latent Dirichlet Allocation (LDA) in this study succeeded in dividing the comments into several topics that were often discussed from Indonesian and English comments regarding the hotel services provided. By using Support Vector Machine (SVM) obtained the number of positive, negative and neutral comments.

References


Marreese Taylor, E., Velasquez, J.D., Bravo-Marquez, F., Matsuo, Y. 2013. Identifying customer preferences about tourism products using an aspect - based opinion mining approach. Procedia Computer Science, 22, pp.182–191.

Taufik, A. 2017. Optimasi Particle Swarm Optimization Sebagai Seleksi Fitur Pada Analisis Sentimen Review Hotel Berbahasa Indonesia Menggunakan Algoritma Naïve Bayes. Jurnal Teknik Komputer. 3(2): 40-47.

Kurniasari, Siti. R. 2018. Implementasi Svm Dan Asosiasi Untuk Sentiment Analysis Data Ulasan the Phoenix Hotel Yogyakarta Pada Situs Tripadvisor. Skripsi. Jurusan Statistika. FMIPA. Universitas Islam Indonesia. Yogyakarta.

Chakraborti, S., 2014. A Comparative Study of Performances of Various Classification Algorithms for Predicting Salary Classes of Employees. International Journal of Computer Science and Information Technologies. Vol. 5 No. 2.

Yuliana S, Afrida H. 2018. Klasifikasi Analisis Sentimen Mengenai Hotel Di Yogyakarta. Jurnal Teknlogi dan Komunikasi. 8(1): 1-10

Annisa, Rossi, Surjandari, Isti & Zulkarnain. 2019. Opinion Mining on Mandalika Hotel Reviews Using Latent Dirichlet Allocation. Procedia Computer Science. 161, pp. 739–746.

Bambang Sugiantoro, Prasdika F. B. S. 2018. A review paper on big data and data mining. IJID International Journal on Informatics for Development. 7(1), Pp. 36-38

Akhtara, Nadeem, Zubaira, Nashez, Kumara, Abhishek & Ahmada, Tameem. 2017. Aspect based Sentiment Oriented Summarization of Hotel Reviews”. Procedia Computer Science. 115 , pp. 563–571.

D. M. Blei. 2003. Latent Dirichlet Allocation. Machine Learning Research 3, pp. 933-1022

J. C. Campbell, A. Hindle and a. E. Stroulia, 2014. Latent Dirichlet Allocation: Extracting Topics.

Suyanto. 2017. Data Mining Untuk Klasifikasi dan Klasterisasi Data. Informatika Bandung. ISBN: 978-602-6232-36-6. Bandung.

Kemenparekraf. 2013. Peraturan Menteri Pariwisata Dan Ekonomi Kreatif Republik Indonesia Standar Usaha Hotel. Nomor Pm.53/Hm.001/Mpek/2013. 2013.


Full Text: PDF

DOI: 10.33751/komputasi.v20i2.7604 Abstract views : 128 views : 169

Refbacks

  • There are currently no refbacks.


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