For the rapidly increasing amount of information available on the Internet, little quality control exists, especially over the user-generated content. Manually scanning through large amounts of user-generated content is time-consuming and sometime impossible. In this case, opinion mining is a better alternative. Although it is recognized that the opinion reviews contain valuable information for a variety of applications, the lack of quality control attracts spammers who have found many ways to draw their benefits from spamming. Moreover, the spam detection problem is complex because spammers always invent fresh methods that can't be easily recognized. The work of this book presents a new approach for performing spam detection in Arabic opinion reviews by merging methods from data mining and text mining in one mining classification approach. Classification algorithms are applied on TBA dataset, ATBA corpus, and ATBAH dataset. The experimental results show that the proposed approach is effective in identifying Arabic spam opinion reviews. Our designed machine learning achieves significant improvements. In the best case, our F-measure is improved up to 99.59%.