Analyzing the Behavior and Text Posted by Users to Extract Knowledge

With the explosion of Web 2.0 platforms such as blogs, discussion forums, andsocial networks, Internet users can express their feelings and share information among themselves. This behavior leads to an accumulation of an enormousamount of information.Among these platforms are so-called microblogs. Microblogging(e.g. Twitter1), as a new form of online communication in whichusers talk about their daily lives, publish opinions or share information by short posts, hasbecome one of the most popular social networking services today, which makes it potentially alarge information base attracting increasing attention of researchers in the field of knowledgediscovery and data mining.Several works have proposed tools for tweets search, but, this area is still not well exploited. Our work consists of examining the role and impact of social networks, in particular microblogs, on public opinion. We aim to analyze the behavior and text posted by users to extract knowledge that reflect the interests and opinions of a population.This gave us the idea to offer new tool more developed that uses new features such as audience and RetweetRank for ranking relevant tweets. We investigate the impact of these criteria on the search’s results for relevant information. Finally, we propose a new metric to improve the results of the searches in microblogs. More accurately, we propose a research model that combines content relevance, tweet relevance and author relevance. Each type of relevance is characterized by a set of criteria such as audience to assess the relevance of the author, OOV (Out Of Vobulary) to measure the relevance of content and others. To evaluate our model, we built a knowledge management system. We used a collection of subjective tweets talking about Tunisian actualities in 2012.