Fake review detection in e-Commerce platforms using aspect-based sentiment analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920757" target="_blank" >RIV/00216275:25410/23:39920757 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0148296323005027" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0148296323005027</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jbusres.2023.114143" target="_blank" >10.1016/j.jbusres.2023.114143</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fake review detection in e-Commerce platforms using aspect-based sentiment analysis

Popis výsledku v původním jazyce

Consumers rely on internet user reviews. Existing sentiment-based detection systems fail to capture consumer feelings regarding numerous aspects of products or services which influence their purchasing decisions. Despite the growing interest in detecting false reviews, prior studies have not explored the capacity to detect fake reviews for diverse products, which require distinct consumer experience. To overcome these problems, this paper proposes a fake review detection model using aspect-based sentiment analysis (ABSA) while considering the effects of product types. Using a dataset of Amazon reviews, our ABSA model revealed that two aspects are fundamental for detecting fake reviews and suggests the need to associate the two. These are the product category and the verified purchase attribute (with the greatest contribution observed for credence and experience product types).

Název v anglickém jazyce

Fake review detection in e-Commerce platforms using aspect-based sentiment analysis

Popis výsledku anglicky

Consumers rely on internet user reviews. Existing sentiment-based detection systems fail to capture consumer feelings regarding numerous aspects of products or services which influence their purchasing decisions. Despite the growing interest in detecting false reviews, prior studies have not explored the capacity to detect fake reviews for diverse products, which require distinct consumer experience. To overcome these problems, this paper proposes a fake review detection model using aspect-based sentiment analysis (ABSA) while considering the effects of product types. Using a dataset of Amazon reviews, our ABSA model revealed that two aspects are fundamental for detecting fake reviews and suggests the need to associate the two. These are the product category and the verified purchase attribute (with the greatest contribution observed for credence and experience product types).

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/GA22-22586S" target="_blank" >GA22-22586S: Aspektově orientovaná analýza sentimentu finančních textů pro predikci finanční výkonnosti podniku</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Journal of Business Research

ISSN

0148-2963

e-ISSN

1873-7978

Svazek periodika

167

Číslo periodika v rámci svazku

listopad

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

114143

Kód UT WoS článku

001039646900001

EID výsledku v databázi Scopus

2-s2.0-85166630721