Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F20%3A39916672" target="_blank" >RIV/00216275:25410/20:39916672 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00521-020-04757-2" target="_blank" >https://link.springer.com/article/10.1007/s00521-020-04757-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-020-04757-2" target="_blank" >10.1007/s00521-020-04757-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining

Popis výsledku v původním jazyce

Fake consumer review detection has attracted much interest in recent years owing to the increasing number of Internet purchases. Existing approaches to detect fake consumer reviews use the review content, product and reviewer information and other features to detect fake reviews. However, as shown in recent studies, the semantic meaning of reviews might be particularly important for text classification. In addition, the emotions hidden in the reviews may represent another potential indicator of fake content. To improve the performance of fake review detection, here we propose two neural network models that integrate traditional bag-of-words as well as the word context and consumer emotions. Specifically, the models learn document-level representation by using three sets of features: (1) n-grams, (2) word embeddings and (3) various lexicon-based emotion indicators. Such a high-dimensional feature representation is used to classify fake reviews into four domains. To demonstrate the effectiveness of the presented detection systems, we compare their classification performance with several state-of-the-art methods for fake review detection. The proposed systems perform well on all datasets, irrespective of their sentiment polarity and product category.

Název v anglickém jazyce

Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining

Popis výsledku anglicky

Fake consumer review detection has attracted much interest in recent years owing to the increasing number of Internet purchases. Existing approaches to detect fake consumer reviews use the review content, product and reviewer information and other features to detect fake reviews. However, as shown in recent studies, the semantic meaning of reviews might be particularly important for text classification. In addition, the emotions hidden in the reviews may represent another potential indicator of fake content. To improve the performance of fake review detection, here we propose two neural network models that integrate traditional bag-of-words as well as the word context and consumer emotions. Specifically, the models learn document-level representation by using three sets of features: (1) n-grams, (2) word embeddings and (3) various lexicon-based emotion indicators. Such a high-dimensional feature representation is used to classify fake reviews into four domains. To demonstrate the effectiveness of the presented detection systems, we compare their classification performance with several state-of-the-art methods for fake review detection. The proposed systems perform well on all datasets, irrespective of their sentiment polarity and product category.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

2020

Kód důvěrnosti údajů

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

Název periodika

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

—

Svazek periodika

32

Číslo periodika v rámci svazku

23

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

17259-17274

Kód UT WoS článku

000510362100001

EID výsledku v databázi Scopus

2-s2.0-85078922732