A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services

Manuel J. Sánchez-Franco, Antonio Navarro-García and Francisco Javier Rondán-Cataluña

Journal of Business Research, 2019, vol. 101, issue C, 499-506

Abstract: This research assesses whether terms related to guest experience can be used to identify ways to enhance hospitality services. A study was conducted to empirically identify relevant features to classify customer satisfaction based on 47,172 reviews of 33 Las Vegas hotels registered with Yelp, a social networking site. The resulting model can help hotel managers understand guests' satisfaction. In particular, it can help managers process vast amounts of review data by using a supervised machine learning approach. The naive algorithm classifies reviews of hotels with high precision and recall and with a low computational cost. These results are more reliable and accurate than prior statistical results based on limited sample data and provide insights into how hotels can improve their services based on, for example, staff experience, professionalism, tangible and experiential factors, and gambling-based attractions.

Keywords: Online reviews; Hospitality services; Customer satisfaction; Text analytics; Classification models; Naive Bayes classifier (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:101:y:2019:i:c:p:499-506

DOI: 10.1016/j.jbusres.2018.12.051

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