Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection

Petr Hajek and Jean-Michel Sahut

Technological Forecasting and Social Change, 2022, vol. 177, issue C

Abstract: Online reviews are increasingly recognized as a key source of information influencing consumer behavior. This in turn implies that competitive advantage can be achieved by manipulating users’ perceptions about restaurants. The hospitality industry is particularly susceptible to this issue because products and services in this industry can only be rated upon consumption. Therefore, many efforts have recently been dedicated to developing automatic methods for detecting fake reviews based on data intelligence in this sector. Recent studies suggest that both the semantic meaning of consumer reviews and the sentiment conveyed may be useful indicators of fake reviews. However, the semantic meaning may be context-sensitive and may also disregard sentiment information. Moreover, the content analysis approach should be integrated with the reviewer's behavior to reveal their true intentions. To address these problems, we propose a review representation model based on behavioural and sentiment-dependent linguistic features that effectively exploit the domain context. Using a large dataset of Yelp restaurant reviews, we demonstrate that the proposed review representation model is more effective than existing approaches in terms of detection accuracy. It furthermore accurately estimates the average rating assigned by legitimate reviewers, which has significant managerial implications for the hospitality industry.

Keywords: fake review; detection; data intelligence; sentiment; behaviour; machine learning model; restaurant; hospitality industry (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:177:y:2022:i:c:s0040162522000646

DOI: 10.1016/j.techfore.2022.121532

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