An improved social media behavioral analysis using deep learning techniques

Abstract

Most online users share their opinions and comments or give their valuable feedbacks on a variety of subjects. Public opinions and comments in social media have had great impact on social and political systems. This vast information can be reviewed and analyzed. As this online information grows in numbers it requires efficient processing. Thus, this information can be mined or analyzed effectively, making it a suitable candidate for data mining. Twitter’s micro blogging service has more than 250 million active users who post short messages about any topic. This vast information is a meaningful source of information regarding different aspects of. This paper proposes to mine and extract information from tweets called IBADL (Improved Behavioral Analysis using Deep Learning), the goal of the proposed technique is to mine information through the study of the tweets posted and conduct an analysis for drawing meaningful conclusions about the behavior of Twitter users.