Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F22%3A39919465" target="_blank" >RIV/00216275:25410/22:39919465 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0040162522000646" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0040162522000646</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.techfore.2022.121532" target="_blank" >10.1016/j.techfore.2022.121532</a>
Alternative languages
Result language
angličtina
Original language name
Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
<a href="/en/project/GA19-15498S" target="_blank" >GA19-15498S: Modelling emotions in verbal and nonverbal managerial communication to predict corporate financial risk</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Technological Forecasting and Social Change
ISSN
0040-1625
e-ISSN
1873-5509
Volume of the periodical
177
Issue of the periodical within the volume
Neuveden
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
121532
UT code for WoS article
000827438400012
EID of the result in the Scopus database
2-s2.0-85123781663