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Hotel online reviews: creating a multi-source aggregated index

Nuno Antonio (Department of Science and Technology, ISCTE-Instituto Universitário de Lisboa, Lisbon, Portugal)
Ana Maria de Almeida (Department of Science and Information Technology, ISCTE-Instituto Universitario de Lisboa, Lisbon, Portugal and Universidade de Coimbra Centro de Informatica e Sistemas, Coimbra, Portugal and ISTAR-IUL, Information Sciences, Technologies and Architecture Research Center, Lisbon, Portugal)
Luís Nunes (Department of Science and Information Technology, ISCTE-Instituto Universitario de Lisboa, Lisbon, Portugal and Instituto de Telecomunicacoes, Lisbon, Portugal and ISTAR-IUL, Information Sciences, Technologies and Architecture Research Center, Lisbon, Portugal)
Fernando Batista (Department of Science and Information Technology, ISCTE-Instituto Universitario de Lisboa, Lisbon, Portugal and Spoken Language Systems Lab, Instituto de Engenharia de Sistemas e Computadores, Lisbon, Portugal)
Ricardo Ribeiro (ISCTE-Instituto Universitario de Lisboa, Lisbon, Portugal and Spoken Language Systems Lab, Instituto de Engenharia de Sistemas e Computadores, Lisbon, Portugal)

International Journal of Contemporary Hospitality Management

ISSN: 0959-6119

Publication date: 10 December 2018

Abstract

Purpose

This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems.

Design/methodology/approach

This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating.

Findings

Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment.

Originality/value

This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.

Keywords

Citation

Antonio, N., de Almeida, A., Nunes, L., Batista, F. and Ribeiro, R. (2018), "Hotel online reviews: creating a multi-source aggregated index", International Journal of Contemporary Hospitality Management, Vol. 30 No. 12, pp. 3574-3591. https://doi.org/10.1108/IJCHM-05-2017-0302

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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