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Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques

Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques
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Author(s): Elshrif Ibrahim Elmurngi (École de Technologie Supérieure, Montreal, Canada) and Abdelouahed Gherbi (École de Technologie Supérieure, Montreal, Canada)
Copyright: 2020
Volume: 10
Issue: 1
Pages: 31
Source title: International Journal of Organizational and Collective Intelligence (IJOCI)
Editor(s)-in-Chief: Victor Chang (Teesside University, UK)
DOI: 10.4018/IJOCI.2020010103

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Abstract

Online reputation systems are a novel and active part of e-commerce environments such as eBay, Amazon, etc. These corporations use reputation reporting systems for trust evaluation by measuring the overall feedback ratings given by buyers, which enables them to compute the reputation score of their products. Such evaluation and computation processes are closely related to sentiment analysis and opinion mining. These techniques incorporate new features into traditional tasks, like polarity detection for positive or negative reviews. The “all excellent reputation” problem is common in the e-commerce domain. Another problem is that sellers can write unfair reviews to endorse or reject any targeted product since a higher reputation leads to higher profits. Therefore, the purpose of the present work is to use a statistical technique for excluding unfair ratings and to illustrate its effectiveness through simulations. Also, the authors have calculated reputation scores from users' feedback based on a sentiment analysis model (SAM). Experimental results demonstrate the effectiveness of the approach.

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