Sparse principal component analysis for natural language processing

Drikvandi, Reza; Lawal, Olamide

doi:10.1007/s40745-020-00277-x

Sparse principal component analysis for natural language processing

Drikvandi, Reza; Lawal, Olamide

Authors

Dr Reza Drikvandi reza.drikvandi@durham.ac.uk
Associate Professor

Olamide Lawal

Abstract

High dimensional data are rapidly growing in many different disciplines, particularly in natural language processing. The analysis of natural language processing requires working with high dimensional matrices of word embeddings obtained from text data. Those matrices are often sparse in the sense that they contain many zero elements. Sparse principal component analysis is an advanced mathematical tool for the analysis of high dimensional data. In this paper, we study and apply the sparse principal component analysis for natural language processing, which can effectively handle large sparse matrices. We study several formulations for sparse principal component analysis, together with algorithms for implementing those formulations. Our work is motivated and illustrated by a real text dataset. We find that the sparse principal component analysis performs as good as the ordinary principal component analysis in terms of accuracy and precision, while it shows two major advantages: faster calculations and easier interpretation of the principal components. These advantages are very helpful especially in big data situations.

Citation

Drikvandi, R., & Lawal, O. (2023). Sparse principal component analysis for natural language processing. Annals of Data Science, 10(1), 25-41. https://doi.org/10.1007/s40745-020-00277-x

Journal Article Type	Article
Acceptance Date	Apr 30, 2020
Online Publication Date	May 18, 2020
Publication Date	2023-02
Deposit Date	Oct 6, 2020
Publicly Available Date	Jan 25, 2023
Journal	Annals of Data Science
Print ISSN	2198-5804
Electronic ISSN	2198-5812
Publisher	Springer
Peer Reviewed	Peer Reviewed
Volume	10
Issue	1
Pages	25-41
DOI	https://doi.org/10.1007/s40745-020-00277-x

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