@conference { ISI:000365128500017, title = {CIOSOS: Combined Idiomatic-Ontology Based Sentiment Orientation System for Trust Reputation in E-commerce}, booktitle = {INTERNATIONAL JOINT CONFERENCE: CISIS{\textquoteright}15 AND ICEUTE{\textquoteright}15}, series = {Advances in Intelligent Systems and Computing}, volume = {369}, year = {2015}, note = {8th International Conference on Computational Intelligence in Security for Information Systems (CISIS) / 6th International Conference on EUropean Transnational Education (ICEUTE), Burgos, SPAIN, JUN, 2015}, pages = {189-200}, publisher = {IEEE Spain Sect; IEEE Syst, Man \& Cybernet Spanish Chapter; Int Federat Computat Log}, organization = {IEEE Spain Sect; IEEE Syst, Man \& Cybernet Spanish Chapter; Int Federat Computat Log}, abstract = {Due to the abundant amount of Customer{\textquoteright}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{\textquoteright}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 {\textquoteleft}{\textquoteleft}opinion indicators{{\textquoteright}{\textquoteright}} as well as {\textquoteleft}{\textquoteleft}idiomatic expressions{{\textquoteright}{\textquoteright}}. 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 {\textquoteleft}{\textquoteleft}granular{{\textquoteright}{\textquoteright}} reputation score of a feature or subfeature of the product.}, isbn = {978-3-319-19713-5; 978-3-319-19712-8}, issn = {2194-5357}, doi = {10.1007/978-3-319-19713-5\_17}, author = {Rahimi, Hasnae and El Bakkali, Hanan}, editor = {Herrero, A and Baruque, B and Sedano, J and Quintian, H and Corchado, E} } @article {10115338520131101, title = {A New Feedback-Analysis based Reputation Algorithm for E-Commerce Communities.}, journal = {E-Ti: E-Review in Technologies Information}, number = {7}, year = {2013}, pages = {46 - 58}, abstract = {Dealing with the ever-growing content generated by users in the e-commerce applications, Trust Reputation Systems (TRS) are widely used online to provide the trust reputation of each product using the customers{\textquoteright} ratings. However, there is also a good number of online customer reviews and feedback that must be used by the TRS. As a result, we propose in this work a new architecture for TRS in e-commerce application which includes feedback{\textquoteright} mining in order to calculate reputation scores. This architecture is based on an intelligent layer that proposes to each user (i.e. {\guillemotleft}feedback provider{\guillemotright}) who has already given his recommendation, a collection of prefabricated feedback to like or dislike. Then the proposed reputation algorithm calculates the trust degree of the user, the feedback{\textquoteright}s trustworthiness and generates the global reputation score of the product according to his {\guillemotleft}likes{\guillemotright} and {\guillemotleft}dislikes{\guillemotright}. In this work, we present also a state of the art of text mining tools and algorithms that can}, keywords = {Algorithms, analyse de sentiment, e-commerce, Electronic commerce, La confiance, le e-commerce, le textmining, les feedback textuels, les syst{\`e}mes de r{\'e}putation, Reliability (Personality trait), Sentiment analysis, text mining, Text mining (Information retrieval), textual feedback, Trust, Trust Reputation Systems, Virtual communities}, issn = {11148802}, url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=101153385\&site=ehost-live}, author = {Rahimi, Hasnae and El Bakkali, Hanan} } @conference { ISI:000327787500003, title = {A New Reputation Algorithm for Evaluating Trustworthiness in E-Commerce Context}, booktitle = {2013 NATIONAL SECURITY DAYS (JNS3)}, year = {2013}, note = {3rd National Security Days (JNS), Mohammed V Souissi Unvi, Rabat, MOROCCO, APR 26-27, 2013}, publisher = {Assoc Marocaine ConfiAnace Numerique; Ecole Nationale Superieure Informatique \& Analyse Syst; Informat Secur Res Team; IEEE Morocco Sect; Bank Al Maghrib; Natl Ctr Sci \& Technol Res}, organization = {Assoc Marocaine ConfiAnace Numerique; Ecole Nationale Superieure Informatique \& Analyse Syst; Informat Secur Res Team; IEEE Morocco Sect; Bank Al Maghrib; Natl Ctr Sci \& Technol Res}, abstract = {Thanks to their ability to detect fraud, poor quality and ill-intentioned feedbacks and scores in online environments, robust Trust Reputation Systems (TRS) provide actionable information to support relying parties taking the right decision in any electronic transaction. In fact, as security providers in e-services, TRS have to faithfully calculate the most trustworthy score for a targeted product or service. Thus, TRS must rely on a robust architecture and suitable algorithms that are able to select, store, generate and classify scores and feedbacks. In this work, we propose a new architecture for TRS in e-commerce application which includes feedbacks{\textquoteright} analysis in its treatment of scores. In fact, this architecture is based on an intelligent layer that proposes to each user (i.e. {\textquoteleft}{\textquoteleft}feedback provider{{\textquoteright}{\textquoteright}}) who has already given his recommendation, a collection of prefabricated feedbacks summarizing other users{\textquoteright} textual feedbacks. A proposed algorithm is used by this architecture in order to calculate the trust degree of the user, the feedback{\textquoteright}s trustworthiness and generates the global reputation score of the product.}, isbn = {978-1-4799-0324-5}, author = {Rahimi, Hasnae and El Bakkali, Hanan} } @conference { ISI:000310353000171, title = {Towards a New Design for Trust Reputation System}, booktitle = {2012 INTERNATIONAL CONFERENCE ON MULTIMEDIA COMPUTING AND SYSTEMS (ICMCS)}, year = {2012}, note = {International Conference on Multimedia Computing and Systems (ICMCS), Tangiers, MOROCCO, MAY 10-12, 2012}, pages = {943-948}, publisher = {Morocco Sect MTT/AP Joint Chapter}, organization = {Morocco Sect MTT/AP Joint Chapter}, abstract = {Trust is indispensible for any user of an e-service in order to make a decision before dealing with any transaction. That{\textquoteright}s the reason why, users and service providers need various and functional methods to build on-line trust reputation systems. This paper discusses the use of trust management and reputation systems in electronic transactions and particularly in e-commerce applications. It presents a survey of some existing trust reputation systems used in e-commerce applications. This survey proposes a new design for trust reputation systems (TRS) that focuses on the use of semantic feedbacks in order to calculate users{\textquoteright} recommendation weights and to classify them according to these weights. This paper highlights the importance of making the distinction between trustful feedbacks or ratings and distrustful ones. It proposes also some methods to follow and to put in practice in order to give the right weight to the right recommendations.}, isbn = {978-1-4673-1520-3}, author = {Rahimi, Hasnae and El Bakkali, Hanane}, editor = {Essaaidi, M and Zaz, Y} }