SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection

TL;DR

SeNMFk-SPLIT is a scalable distributed approach for semantic topic modeling of large text corpora, improving upon previous NMF-based methods by enabling joint factorization of large matrices for comprehensive analysis.

Contribution

It introduces a novel distributed method, SeNMFk-SPLIT, allowing large-scale semantic topic modeling by separately factorizing matrices, suitable for extensive datasets like arXiv AI/ML literature.

Findings

Successfully applied to entire arXiv AI/ML literature

Enables joint factorization of large matrices

Improves scalability of semantic NMF methods

Abstract

As the amount of text data continues to grow, topic modeling is serving an important role in understanding the content hidden by the overwhelming quantity of documents. One popular topic modeling approach is non-negative matrix factorization (NMF), an unsupervised machine learning (ML) method. Recently, Semantic NMF with automatic model selection (SeNMFk) has been proposed as a modification to NMF. In addition to heuristically estimating the number of topics, SeNMFk also incorporates the semantic structure of the text. This is performed by jointly factorizing the term frequency-inverse document frequency (TF-IDF) matrix with the co-occurrence/word-context matrix, the values of which represent the number of times two words co-occur in a predetermined window of the text. In this paper, we introduce a novel distributed method, SeNMFk-SPLIT, for semantic topic extraction suitable for large corpora. Contrary to SeNMFk, our method enables the joint factorization of large documents by decomposing the word-context and term-document matrices separately. We demonstrate the capability of SeNMFk-SPLIT by applying it to the entire artificial intelligence (AI) and ML scientific literature uploaded on arXiv.