An end-to-end ranking system based on customers reviews: Integrating semantic mining and MCDM techniques

Eshkevari, M; Jahangoshai Rezaee, M; Saberi, M; Hussain, OK

An end-to-end ranking system based on customers reviews: Integrating semantic mining and MCDM techniques

Eshkevari, M Jahangoshai Rezaee, M Saberi, M

Hussain, OK

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2022, 209
Issue Date:: 2022-12-15

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Field	Value	Language
dc.contributor.author	Eshkevari, M
dc.contributor.author	Jahangoshai Rezaee, M
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Hussain, OK
dc.date.accessioned	2023-03-20T01:12:54Z
dc.date.available	2023-03-20T01:12:54Z
dc.date.issued	2022-12-15
dc.identifier.citation	Expert Systems with Applications, 2022, 209
dc.identifier.issn	0957-4174
dc.identifier.issn	1873-6793
dc.identifier.uri	http://hdl.handle.net/10453/167685
dc.description.abstract	Considering customer reviews is one of the challenges of real-world decision models. These reviews can be on different platforms and include a large amount of information and may also include incomprehensible and unrelated phrases. The main advantage of customer reviews is the realistic view that it provides a realistic view of the product or service. Therefore, converting unstructured and incoherent customer-based reviews into machine learning language and ultimately turning it into a decision model is very important. In this paper, we propose an end-to-end ranking method for integrating mechanisms such as text processing, sentiment analysis and the multi-criteria decision-making technique. The proposed ranking method relies on the integration of three methods, namely, the aspect-based sentiment analysis (ABSA) method, the Dawid-Skene algorithm and the Best Worst Method (BWM). In other words, the proposed work encompasses four major steps: i) crawling customer reviews, ii) preprocessing, iii) aspect term extraction, aspect category detection and polarity detection, and iv) designing a decision-making model. The main contribution of this study is to consider ABSA at three levels simultaneously and integrate ABSA and BWM in designing an end-to-end ranking method for ranking the quality of hotel services, facilities and amenities based on customer reviews. The ability of the proposed end-to-end ranking framework is evaluated using a real data set of user reviews of Sydney hotels.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Expert Systems with Applications
dc.relation.isbasedon	10.1016/j.eswa.2022.118294
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	An end-to-end ranking system based on customers reviews: Integrating semantic mining and MCDM techniques
dc.type	Journal Article
utslib.citation.volume	209
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-20T01:12:53Z
pubs.publication-status	Published
pubs.volume	209

Abstract:

Considering customer reviews is one of the challenges of real-world decision models. These reviews can be on different platforms and include a large amount of information and may also include incomprehensible and unrelated phrases. The main advantage of customer reviews is the realistic view that it provides a realistic view of the product or service. Therefore, converting unstructured and incoherent customer-based reviews into machine learning language and ultimately turning it into a decision model is very important. In this paper, we propose an end-to-end ranking method for integrating mechanisms such as text processing, sentiment analysis and the multi-criteria decision-making technique. The proposed ranking method relies on the integration of three methods, namely, the aspect-based sentiment analysis (ABSA) method, the Dawid-Skene algorithm and the Best Worst Method (BWM). In other words, the proposed work encompasses four major steps: i) crawling customer reviews, ii) preprocessing, iii) aspect term extraction, aspect category detection and polarity detection, and iv) designing a decision-making model. The main contribution of this study is to consider ABSA at three levels simultaneously and integrate ABSA and BWM in designing an end-to-end ranking method for ranking the quality of hotel services, facilities and amenities based on customer reviews. The ability of the proposed end-to-end ranking framework is evaluated using a real data set of user reviews of Sydney hotels.

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http://hdl.handle.net/10453/167685