Spectral analysis and text processing over the computer science literature: patterns and discoveries.

Abstract

We defend the thesis that the use of text analytics can boost the results of analyses based on Singular Value Decomposition (SVD). To demonstrate our supposition, first we model the Digital Bibliography & Library Project (DBLP) as a relational schema; over this schema we use text analytics applied to the terms extracted from the titles of the articles. Then, we apply SVD on the relationships defined between these terms, publication vehicles, and authors; accordingly, we were able to identify the more representative communities and the more active authors relating them to the most meaningful terms and topics found in their respective publications. The results were semantically dense and concise, also leading to performance gains.