A novel model for analyzing online customer experience in hotel services approach by topic modeling

Abstract

Recently, with the growth of technology and the Internet, customers can easily give their opinions and feedback about products and services on websites or social media. This information is stored in text 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 99,322 customer comments and reviews written in English from some e-commerce websites in the hospitality industry. After pre-processing the collected data, our team conducted experiments on this corpus and chose the best number of topics (K) was chosen by Perplexity and Coherence Score measurements as input parameters for the model. Finally, experiment on the corpus was used based on the Latent Dirichlet Allocation (LDA) model with K coefficient to explore the topic. The model results found hidden topics with a 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 hospitality sector.