Rokne, Jon G.Rachdi, Btissam2019-05-302019-05-302019-05-21Rachdi, B. (2019). Fuzzy Logic Classification in Review Spam Detection (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/110448With the recent popularity of e-commerce, customers publish reviews about the products or services they purchased or utilized and these reviews in turn serve as the means for the potential customers to make a better choice based on the experiences of others. These pieces of opinion information are not only important for individual users but also benefit the business organizations, as they can monitor the customers’ opinions, and accordingly adjust their business strategies. However, many of the reviewing systems exploit this motivation for some people to enter their fake reviews to promote some products or defame some others. Hence, in recent years, review analysis has gained a lot of importance and by using opinion-mining detection; I could locate and eliminate potential spam reviews. In this thesis, I have introduced fuzzy logic in the review spam detection and combined two others data mining techniques, periodicity of frequent pattern and the outlier detection to study the behavior of the reviewer towards the reviewed product and classify the users using the fuzzy logic classification model. Thus, the proposed analysis have been proposed and examined over a sample of dataset.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.review spam detection, frequent pattern mining, outlier detectionComputer Sciencemaster thesis10.11575/PRISM/36607