Interactive Visual Analysis of Temporal Text Data

Abstract

This paper presents a novel interactive visualization technique that helps in gathering insights from large volumes of text generated through dyadic communications. The emphasis is specifically on showing content evolution and modification with passage of time. The challenge lies in presenting not only the content as a stand-alone but also understand how the present is related to the past. For example analyzing large volumes of emails can show how communication among a set of people have progressed or evolved over time, may be along with the roles of the communicators. It can also show how the content has changed or evolved. In order to depict the changes, the email repositories are first clustered using a novel algorithm. The clusters are further time-stamped and correlated. User-insights are provided through visualization of these clusters. Results of implementation over two different datasets are presented.