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

{"title":"Mining online hotel reviews using big data and machine learning: An empirical study from an emerging country","authors":"Hanh Thi My Le , Thuy-An Phan-Thi , Binh T. Nguyen , Thang Quyet Nguyen","doi":"10.1016/j.annale.2025.100170","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a framework for collecting large datasets of hotel reviews (e.g., from <span><span>Booking.com</span><svg><path></path></svg></span> 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 <span><span>Booking.com</span><svg><path></path></svg></span> 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.</div></div>","PeriodicalId":34520,"journal":{"name":"Annals of Tourism Research Empirical Insights","volume":"6 1","pages":"Article 100170"},"PeriodicalIF":4.0000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Tourism Research Empirical Insights","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666957925000059","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}

引用次数: 0

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.

Abstract Image