No abstract

Text Mining is a set of techniques that analyzes large masses of data, extract relations that are unknown beforehand, and provide solutions to help decision-making. Text mining had been used extensively to analyze English text. However, text mining has only been used recently in analyzing Arabic text. As a result the objective of this paper is to present the current state of Arabic text mining. A systematic review has been performed to collect the papers published on the analysis of Arabic text mining. More than one hundred papers were used in our review from different reliable sources, and then they were classified according to their specific domain, and classified again according to the specific techniques used. This paper also provides quantitative analysis of publications according to publication type, year, category, and contributors.

Lately, social networks have become a vital part of our lives. Among many different uses, most people use social networks to communicate and stay informed. Twitter, a microblogging site, is currently one of the most popular social network sites. Users follow different accounts such as friends, celebrities, or companies to get information through 280 character messages (or tweets). There are currently 1.3 billion registered users on Twitter with 330 million of them active users generating 500 million tweets daily [1]. Analyzing Twitter content has recently gained a lot of attention due to its popularity all over the world and the significance of its content in detecting patterns and inferring hidden information. A significant portion of analyzing Twitter content goes into analyzing the opinions and behavior of its users. Detecting similarity between users based on their produced content, behavior, interests, and activities is an important application of Twitter content analysis as could be seen in [2-4]. Detecting similarity could be used in profiling users for security, recruitment and social reasons. Governments could use it to identify persons who impose a threat to the security of their people by identifying one individual and finding others similar to her/him. Businesses could benefit from it in recruitment and target marketing by identifying candidate Twitter users and finding similar ones to them. Individuals could use it to find similar users to them or to others who they are interested in.