# Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization

arXiv: https://arxiv.org/abs/2201.00687 · HTML: https://mlsanity.com/paper/2201.00687/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)
- [Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis](https://mlsanity.com/paper/1505.07302/) (2015)

## Built on by (most cited first, 1 of 1)

- [Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization](https://mlsanity.com/paper/2403.16222/) (2024)

## Abstract (arXiv)

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.