An end-to-end ranking system based on customers reviews: Integrating semantic mining and MCDM techniques. (15th December 2022)

• Record Type: Journal Article
• Authors: Eshkevari, Milad; Jahangoshai Rezaee, Mustafa; Saberi, Morteza; Hussain, Omar K.
• Is Part Of: Expert systems with applications. Volume 209(2022)
• Publication Date: 2022-12-15
• Subjects: End-to-end ranking -- Aspect-based sentiment analysis -- Dawid-skene algorithm -- Best-worst method -- Customer reviews -- Sydney hotels ranking; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118294
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Considering ABSA at three levels simultaneously based on customer reviews. Integrating ABSA and BWM in designing an end-to-end ranking system. Ranking the quality of services, facilities and amenities based on real case study. Supporting customers decision by addressing the issue of "choice overload" Designing an end-to-end ranking method as a service for web users. 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.