Title: Improving emotion classification of the hotel's customer satisfaction online reviews: a self-adaptive ensemble approach

Authors: Xin Li; Ding-Bang Luh; Zi-Hao Chen; Yue Sun; Guanyu Pan; Qianer Li

Addresses: School of Art and Design, Guangdong University of Technology, China ' School of Art and Design, Guangdong University of Technology, China ' School of Art and Design, Guangdong University of Technology, China ' School of Art and Design, Guangdong University of Technology, China ' College of Mathematics and Informatics, South China Agricultural University, Guangdong, China ' College of Mathematics and Informatics, South China Agricultural University, Guangdong, China

Abstract: With the evolution of the tourism industry, updating the emotion classification model using artificial intelligence approaches become essential. This article aims to address the issue that hotel online reviews of customer satisfaction are affected by many attributes. To test the proposed framework, this article utilised over 1,483 online reviews of Hainan Island hotels on TripAdvisor.com to extract accommodation factors and sentiment terms. The TF-IDF term weighting integration module was then applied. Finally, a self-adaptive ensemble model for criticism was built based on the existing database. This study provides an example of how machine learning models can be applied to improve hotel accommodation service quality. Moreover, the differentiation of positive and negative comments by artificial intelligence tools allows for the handling of countless statistics that were previously impossible. The research approaches are significant for studying customer satisfaction, particularly in the context of the tourism industry's economic recovery.

Keywords: text mining; self-adaptive ensemble model; hotel service development; online reviews; customer satisfaction.

DOI: 10.1504/IJCSYSE.2024.142765

International Journal of Computational Systems Engineering, 2024 Vol.8 No.3/4, pp.148 - 159

Received: 11 Apr 2023
Accepted: 11 Jun 2023
Published online: 21 Nov 2024 *

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