Mining online hotel reviews using big data and machine learning: An empirical study from an emerging country

IF 4 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM

Annals of Tourism Research Empirical Insights Pub Date : 2025-03-06 DOI:10.1016/j.annale.2025.100170

Hanh Thi My Le , Thuy-An Phan-Thi , Binh T. Nguyen , Thang Quyet Nguyen

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Abstract

This paper presents a framework for collecting large datasets of hotel reviews (e.g., from Booking.com and TripAdvisor) and performing useful analytics from the data collected. This approach automates data collection, reduces manual effort, enhances data cleaning, and standardizes data processing. We compiled extensive datasets of 607,451 reviews from Booking.com and 782,584 from TripAdvisor, representing the most extensive emerging market-specific hotel review datasets. We conducted statistical analysis to evaluate the review distribution and customer satisfaction levels. Sentiment analysis assessed the polarity and subjectivity of English reviews and their impact on customers' overall satisfaction. Additionally, we used topic modeling with Latent Dirichlet Allocation (LDA) to identify key themes within the reviews to understand customers' real needs, providing helpful insights for hotel management.

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利用大数据和机器学习挖掘在线酒店评论：来自一个新兴国家的实证研究

本文提出了一个框架，用于收集酒店评论的大型数据集（例如，来自Booking.com和TripAdvisor），并对收集到的数据进行有用的分析。这种方法自动化了数据收集，减少了人工工作，增强了数据清理，并标准化了数据处理。我们收集了来自Booking.com的607,451条评论和来自TripAdvisor的782,584条评论的广泛数据集，代表了最广泛的针对新兴市场的酒店评论数据集。我们进行了统计分析，以评估评论的分布和客户满意度水平。情感分析评估了英语评论的极性和主观性及其对顾客总体满意度的影响。此外，我们使用潜在狄利克雷分配（Latent Dirichlet Allocation， LDA）的主题建模来识别评论中的关键主题，以了解客户的真实需求，为酒店管理提供有用的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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