CIOSOS: Combined Idiomatic-Ontology Based Sentiment Orientation System for Trust Reputation in E-commerce

TitreCIOSOS: Combined Idiomatic-Ontology Based Sentiment Orientation System for Trust Reputation in E-commerce
Publication TypeConference Paper
Year of Publication2015
AuthorsRahimi, H, Bakkali, HEl
EditorHerrero, A, Baruque, B, Sedano, J, Quintian, H, Corchado, E
Conference NameINTERNATIONAL JOINT CONFERENCE: CISIS'15 AND ICEUTE'15
PublisherIEEE Spain Sect; IEEE Syst, Man & Cybernet Spanish Chapter; Int Federat Computat Log
ISBN Number978-3-319-19713-5; 978-3-319-19712-8
Abstract

Due to the abundant amount of Customer's Reviews available in E-commerce platforms, Trust Reputation Systems remain reliable means to determine, circulate and restore the credibility and reputation of reviewers and their provided reviews. In fact before starting the process of Reputation score's calculation, we need to develop an accurate Sentiment orientation System able to extract opinion expressions, analyze them and determine the sentiment orientation of the Review and then classify it into positive, negative and objective. In this paper, we propose a novel semi-supervised approach which is a Combined Idiomatic-Ontology based Sentiment Orientation System (CIOSOS) that realizes a domain-dependent sentiment analysis of reviews. The main contribution of the system is to expand the general opinion lexicon SentiWordNet to a custom-made opinion lexicon (SentiWordNet++) with domain-dependent ``opinion indicators{''} as well as ``idiomatic expressions{''}. The system relies also on a semi-supervised learning method that uses the general lexicon WordNet to identify synonyms or antonyms of the expanded terms and get their polarities from SentiWordNet and then store them in SentiWordNet++. The Sentiment polarity and the classification of the review provided by the CIOSOS is used as an input of our Reputation Algorithm proposed in previous papers in order to generate the Reputation score of the reviewer. We also provide an improvement in calculation method used to generate a ``granular{''} reputation score of a feature or subfeature of the product.

DOI10.1007/978-3-319-19713-5\_17
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