Review Spam Detection Using Word Embeddings and Deep Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F19%3A39914919" target="_blank" >RIV/00216275:25410/19:39914919 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-19823-7_28" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-19823-7_28</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-19823-7_28" target="_blank" >10.1007/978-3-030-19823-7_28</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Review Spam Detection Using Word Embeddings and Deep Neural Networks

Popis výsledku v původním jazyce

Review spam (fake review) detection is increasingly important taking into consideration the rapid growth of internet purchases. Therefore, sophisticated spam filters must be designed to tackle the problem. Traditional machine learning algorithms use review content and other features to detect review spam. However, as demonstrated in related studies, the linguistic context of words may be of particular importance for text categorization. In order to enhance the performance of review spam detection, we propose a novel content-based approach that considers both bag-of-words and word context. More precisely, our approach utilizes n-grams and the skip-gram word embedding method to build a vector model. As a result, high-dimensional feature representation is generated. To handle the representation and classify the review spam accurately, a deep feed-forward neural network is used in the second step. To verify our approach, we use two hotel review datasets, including positive and negative reviews. We show that the proposed detection system outperforms other popular algorithms for review spam detection in terms of accuracy and area under ROC. Importantly, the system provides balanced performance on both classes, legitimate and spam, irrespective of review polarity.

Název v anglickém jazyce

Review Spam Detection Using Word Embeddings and Deep Neural Networks

Popis výsledku anglicky

Review spam (fake review) detection is increasingly important taking into consideration the rapid growth of internet purchases. Therefore, sophisticated spam filters must be designed to tackle the problem. Traditional machine learning algorithms use review content and other features to detect review spam. However, as demonstrated in related studies, the linguistic context of words may be of particular importance for text categorization. In order to enhance the performance of review spam detection, we propose a novel content-based approach that considers both bag-of-words and word context. More precisely, our approach utilizes n-grams and the skip-gram word embedding method to build a vector model. As a result, high-dimensional feature representation is generated. To handle the representation and classify the review spam accurately, a deep feed-forward neural network is used in the second step. To verify our approach, we use two hotel review datasets, including positive and negative reviews. We show that the proposed detection system outperforms other popular algorithms for review spam detection in terms of accuracy and area under ROC. Importantly, the system provides balanced performance on both classes, legitimate and spam, irrespective of review polarity.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

IFIP Advances in Information and Communication Technology. Vol. 559

ISBN

978-3-030-19822-0

ISSN

1868-4238

e-ISSN

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Počet stran výsledku

11

Strana od-do

340-350

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Hersonissos

Datum konání akce

24. 5. 2019

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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