@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} }