Researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities. We consider a novel topic diffusion discovery technique thatorporates sparseness-constrained Non-negative Matrix Factorization. This approach can extract more prominent topics from large article databases.
Due to recent explosion of text data, researchers have been overwhelmed by
ever-increasing volume of articles produced by different research communities.
Various scholarly search websites, citation recommendation engines, and
research databases have been created to simplify the text search tasks.
However, it is still difficult for researchers to be able to identify potential
research topics without doing intensive reviews on a tremendous number of
articles published by journals, conferences, meetings, and workshops. In this
paper, we consider a novel topic diffusion discovery technique that
incorporates sparseness-constrained Non-negative Matrix Factorization with
generalized Jensen-Shannon divergence to help understand term-topic evolutions
and identify topic diffusions. Our experimental result shows that this approach
can extract more prominent topics from large article databases, visualize
relationships between terms of interest and abstract topics, and further help
researchers understand whether given terms/topics have been widely explored or
whether new topics are emerging from literature.