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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos; 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&apos;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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos; 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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

<a href="/cs/project/GA19-15498S" target="_blank" >GA19-15498S: Modelování emocí ve verbální a neverbální manažerské komunikaci pro predikci podnikových finančních rizik</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Technological Forecasting and Social Change

ISSN

0040-1625

e-ISSN

1873-5509

Svazek periodika

177

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

121532

Kód UT WoS článku

000827438400012

EID výsledku v databázi Scopus

2-s2.0-85123781663