A hybrid approach for generating reputation based on opinions fusion and sentiment analysis. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Benlahbib, Abdessamad; Nfaoui, El Habib
• Is Part Of: Journal of organizational computing and electronic commerce. Volume 30:Issue 1(2020)
• Page Start: 9
• Page End: 27
• Publication Date: 2020-01-02
• Subjects: Opinion fusion -- opinion mining -- reputation generation -- sentiment analysis and machine learning; Management -- Data processing -- Periodicals; Electronic commerce -- Periodicals; Electronic data interchange -- Periodicals; Business enterprises -- Computer networks -- Periodicals; 658.0505
• DOI: 10.1080/10919392.2019.1654350
• Languages: English
• ISSNs: 1091-9392

Abstract

ABSTRACT: Amazon, eBay, IMDb as well as several websites provide a convenient platform where users share their opinions on any entities without hindrance. Though those opinions are too many to be examined one by one, this is why a general reputation value will help people make a decision toward a target entity (purchase, download, rent …). This fact makes reputation generation task very challenging because an inaccurate reputation system will directly damage the credibility and popularity of the target entity. This paper aims to improve a recent work that handles the task of generating reputation based on fuzing and mining opinions expressed in natural languages and user feedback ratings. Therefore, we have proposed a hybrid approach that, (i) separates reviews into positive and negative based on their sentiment polarity by applying the two classifiers Naïve Bayes and Linear Support Vector Machine (LSVM), (ii) groups positive and negative reviews into principal opinion sets based on their semantic relations, (iii) calculates a custom reputation value separately for positive and negative groups by considering some statistics of principal opinion sets and finally (iv) computes the final reputation value using Weighted Arithmetic Mean. Experimental results show a significant improvement with respect to recent work.