Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization
This provides a nuanced analysis tool for researchers in materials science, though it is incremental as it applies an existing method to a new domain.
The researchers tackled the problem of analyzing superconductivity literature by applying SeNMFk, a semantic non-negative matrix factorization method with topic number determination, to extract coherent topics validated by experts, revealing that most topics map to specific scientific effects or techniques while only three topics appear in nearly 40% of abstracts.
We utilize a recently developed topic modeling method called SeNMFk, extending the standard Non-negative Matrix Factorization (NMF) methods by incorporating the semantic structure of the text, and adding a robust system for determining the number of topics. With SeNMFk, we were able to extract coherent topics validated by human experts. From these topics, a few are relatively general and cover broad concepts, while the majority can be precisely mapped to specific scientific effects or measurement techniques. The topics also differ by ubiquity, with only three topics prevalent in almost 40 percent of the abstract, while each specific topic tends to dominate a small subset of the abstracts. These results demonstrate the ability of SeNMFk to produce a layered and nuanced analysis of large scientific corpora.