Nowadays, millions of products and services are available to the public online. Therefore, searching for the best products which targets the individuals’ requirements would be difficult as the result of the existence of too many offers. One of the most reliable approaches to choose a product or service is to exploit the experiences of the people who have already tried them, and so have reported almost honest opinions about them. A reviewing system is a place where individuals write their reviews on their experienced products and services, and also benefit from others’ reviews. Moreover, companies utilize reviewing systems to apply opinion mining techniques in order to improve their goods or services and to watch their competitors. However, the popularity of the reviewing systems ignites this motivation for some people to enter their fake review to promote some products or defame some others. These review spam should get detected and eliminated in order to prevent misleading potential customers. Opinion mining should be adapted to locate and eliminate potential spam reviews. In this thesis, some review spam detection approaches have been proposed and examined over a sample dataset. The proposed approaches consider the patterns existed in the trends of the reviews, as well as the reviewers’ behaviors. The approaches depend on various strategies such as observing abnormal trends, detecting uncommon or suspicious behaviors, investigating group activities, and so on.