Detecting opinion spams through supervised boosting approach

Product reviews are the individual’s opinions, judgement or belief about a certain product or service provided by certain companies. Such reviews serve as guides for these companies to plan and monitor their business ventures in terms of increasing productivity or enhancing their product/service qua...

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Main Authors:	Mohamad, Hazim, Nor Badrul, Anuar, Mohd Faizal, Ab Razak, Nor Aniza, Abdullah
Format:	Article
Language:	English English
Published:	Public Library of Science 2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/22992/ http://umpir.ump.edu.my/id/eprint/22992/ http://umpir.ump.edu.my/id/eprint/22992/ http://umpir.ump.edu.my/id/eprint/22992/1/Detecting%20opinion%20spams%20through%20supervised%20boosting%20approach.pdf http://umpir.ump.edu.my/id/eprint/22992/7/Detecting%20opinion%20spams%20through%20supervised%20boosting%20approach.pdf

id	ump-22992
recordtype	eprints
spelling	ump-229922018-12-03T08:18:37Z http://umpir.ump.edu.my/id/eprint/22992/ Detecting opinion spams through supervised boosting approach Mohamad, Hazim Nor Badrul, Anuar Mohd Faizal, Ab Razak Nor Aniza, Abdullah QA76 Computer software Product reviews are the individual’s opinions, judgement or belief about a certain product or service provided by certain companies. Such reviews serve as guides for these companies to plan and monitor their business ventures in terms of increasing productivity or enhancing their product/service qualities. Product reviews can also increase business profits by convincing future customers about the products which they have interest in. In the mobile application marketplace such as Google Playstore, reviews and star ratings are used as indicators of the application quality. However, among all these reviews, hereby also known as opinions, spams also exist, to disrupt the online business balance. Previous studies used the time series and neural network approach (which require a lot of computational power) to detect these opinion spams. However, the detection performance can be restricted in terms of accuracy because the approach focusses on basic, discrete and document level features only thereby, projecting little statistical relationships. Aiming to improve the detection of opinion spams in mobile application marketplace, this study proposes using statistical based features that are modelled through the supervised boosting approach such as the Extreme Gradient Boost (XGBoost) and the Generalized Boosted Regression Model (GBM) to evaluate two multilingual datasets (i.e. English and Malay language). From the evaluation done, it was found that the XGBoost is most suitable for detecting opinion spams in the English dataset while the GBM Gaussian is most suitable for the Malay dataset. The comparative analysis also indicates that the implementation of the proposed statistical based features had achieved a detection accuracy rate of 87.43 per cent on the English dataset and 86.13 per cent on the Malay dataset. Public Library of Science 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22992/1/Detecting%20opinion%20spams%20through%20supervised%20boosting%20approach.pdf pdf en http://umpir.ump.edu.my/id/eprint/22992/7/Detecting%20opinion%20spams%20through%20supervised%20boosting%20approach.pdf Mohamad, Hazim and Nor Badrul, Anuar and Mohd Faizal, Ab Razak and Nor Aniza, Abdullah (2018) Detecting opinion spams through supervised boosting approach. PLoS ONE, 13 (6). pp. 1-23. ISSN 1932-6203 https://doi.org/10.1371/journal.pone.0198884 DOI: 10.1371/journal.pone.0198884
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Mohamad, Hazim Nor Badrul, Anuar Mohd Faizal, Ab Razak Nor Aniza, Abdullah Detecting opinion spams through supervised boosting approach
description	Product reviews are the individual’s opinions, judgement or belief about a certain product or service provided by certain companies. Such reviews serve as guides for these companies to plan and monitor their business ventures in terms of increasing productivity or enhancing their product/service qualities. Product reviews can also increase business profits by convincing future customers about the products which they have interest in. In the mobile application marketplace such as Google Playstore, reviews and star ratings are used as indicators of the application quality. However, among all these reviews, hereby also known as opinions, spams also exist, to disrupt the online business balance. Previous studies used the time series and neural network approach (which require a lot of computational power) to detect these opinion spams. However, the detection performance can be restricted in terms of accuracy because the approach focusses on basic, discrete and document level features only thereby, projecting little statistical relationships. Aiming to improve the detection of opinion spams in mobile application marketplace, this study proposes using statistical based features that are modelled through the supervised boosting approach such as the Extreme Gradient Boost (XGBoost) and the Generalized Boosted Regression Model (GBM) to evaluate two multilingual datasets (i.e. English and Malay language). From the evaluation done, it was found that the XGBoost is most suitable for detecting opinion spams in the English dataset while the GBM Gaussian is most suitable for the Malay dataset. The comparative analysis also indicates that the implementation of the proposed statistical based features had achieved a detection accuracy rate of 87.43 per cent on the English dataset and 86.13 per cent on the Malay dataset.
format	Article
author	Mohamad, Hazim Nor Badrul, Anuar Mohd Faizal, Ab Razak Nor Aniza, Abdullah
author_facet	Mohamad, Hazim Nor Badrul, Anuar Mohd Faizal, Ab Razak Nor Aniza, Abdullah
author_sort	Mohamad, Hazim
title	Detecting opinion spams through supervised boosting approach
title_short	Detecting opinion spams through supervised boosting approach
title_full	Detecting opinion spams through supervised boosting approach
title_fullStr	Detecting opinion spams through supervised boosting approach
title_full_unstemmed	Detecting opinion spams through supervised boosting approach
title_sort	detecting opinion spams through supervised boosting approach
publisher	Public Library of Science
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22992/ http://umpir.ump.edu.my/id/eprint/22992/ http://umpir.ump.edu.my/id/eprint/22992/ http://umpir.ump.edu.my/id/eprint/22992/1/Detecting%20opinion%20spams%20through%20supervised%20boosting%20approach.pdf http://umpir.ump.edu.my/id/eprint/22992/7/Detecting%20opinion%20spams%20through%20supervised%20boosting%20approach.pdf
first_indexed	2023-09-18T22:34:15Z
last_indexed	2023-09-18T22:34:15Z
_version_	1777416489614180352

Detecting opinion spams through supervised boosting approach

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