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Recently, with the growth of technology and the Internet, customers can easily create their opinions and feedbacks about products and services of hotels on websites or social media. This information is stored in textual form, and is a huge source of data to explore. In order to continue developing to meet customers' needs, businesses need to gain customers' insights that customers discuss and concern. In this study, we firstly collected a corpus of 26,482 customer comments and reviews written in English from some e-commerce websites in the hospitality industry. After preprocessing the collected data, our team conducted experiments on this corpus and chose the best number of topics (K) by Coherence Score measurements as input parameters for the model. Finally, experiment on the corpus according to the Latent Dirichlet Allocation (LDA) model with K coefficient to explore the topic. The model results found hidden topics with the corresponding list of keywords, reflecting the issues that customers are interested in. Applying empirical results from the model will support decision making to improve products and services in business as well as in the management and development of businesses in the hotel sector.

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Article Details

Issue: Vol 4 No 4 (2020)
Page No.: 1081-1092
Published: Nov 9, 2020
Section: Research article

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Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Ho, N., & Thanh, H. (2020). Topic modeling for analyzing online reviews in hotel sector. VNUHCM Journal of Economics, Business and Law, 4(4), 1081-1092.

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