Automatic Data Analytics for Twitter Social Network

Abstract

Currently social networks represent continuous sources of large volumes of valuable data in different contexts. However, the process of accessing and processing this data involves complex programming tasks. This article presents the design, implementation and evaluation of DAT (Web Data Analytics Tweet), a web application that automates the acquisition, cleaning, filtering, transformation, analysis and visualization of data from the social network Twitter. DAT was modeled under an MVC architecture and implemented using Python. The results show a web environment that with only 3 easy and intuitive panels allows non-programmers users to do and export data analysis from Twitter.