TOPNMF: Topic based Document Clustering using Non-negative Matrix Factorization

R Parimala; K Gomathi

doi:10.17485/IJST/v14i31.1293

Article

TOPNMF: Topic based Document Clustering using Non-negative Matrix Factorization

VIEWS 1890
PDF 434

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i31.1293

Year: 2021, Volume: 14, Issue: 31, Pages: 2590-2595

Original Article

TOPNMF: Topic based Document Clustering using Non-negative Matrix Factorization

R Parimala^1*, K Gomathi²

¹Assistant Professor, Department of Computer Science, Periyar EVR College, Tiruchirapalli, 620023, Tamil Nadu, India
²Research Scholar, Department of Computer Science, Periyar EVR College, Tiruchirapalli, 620023, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:13 July 2021, Accepted Date:26 August 2021, Published Date:27 September 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This work focuses on creating targeted content-specific topicbased clusters. They can help users to discover the topics in a set of documents information more efficiently. Methods/Statistical analysis: The Non-negative Matrix Factorization (NMF) based models learn topics by directly decomposing the term-document matrix, which is a bag-of-word matrix representation of a text corpus, into two low-rank factor matrices namely Word-Topic feature Matrix(WTOM) and Document-Topic feature Matrix(DTOM). Topic clusters and Document clusters are extracted from obtained features matrices. This method does not require any statistical distribution and probability. Experiments were carried out on a subset of BBC sport Corpus. Findings: The experimental results indicate that the accuracy of TONMF clusters was observed as 100 percent. Novelty/Applications: NMF often fails to improve the given clustering result as the number of parameters increases linearly with the size of the corpus. The computational complexity of the TOPNMF is better than exact decomposition like Singular Value Decomposition (SVD).

Keywords: Topic cluster; Document cluster; Non-negative matrix factorization; K-means clustering; Word cloud

References

Blei DM. Probabilistic topic models. Commun ACM. 2012;55(4):77–84. Available from: https://doi.org/10.1145/2133806.2133826
Korshunova I, Xiong H, Fedoryszak M, LT. Discriminative topic modeling with logistic LDA. arXiv. 2019. Available from: http://arxiv.org/abs/1909.01436
Kim H, Park H. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics. 2007;23(12):1495–1502.
Xu W, Liu X, Gong Y. Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval - SIGIR ’03. ACM Press. 2003.
Ailem M, Salah A, Nadif M. Non-negative matrix factorization meets word embedding. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’17. ACM Press. 2017.
Shahbazi Z, Byun YC. Topic modeling in short-text using non-negative matrix factorization based on deep reinforcement learning. J Intell Fuzzy Syst. 2020;39(1):753–770.
Fu X, Huang X, Sidiropoulos ND, Ma WK. Nonnegative matrix factorization for signal and data analytics: Identifiability, algorithms, and applications. arXiv . 2018. Available from: http://arxiv.org/abs/1803.01257
Wang J, Zhang XL. Deep NMF topic modeling. arXiv . 2021. Available from: http://arxiv.org/abs/2102.12998
Shi T, Kang K, Choo J, Reddy CK. Short-text topic modeling via non-negative matrix factorization enriched with local word-context correlations. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW ’18. ACM Press. 2018.
Divaportal.org. 2021. Available from: http://uu.divaportal.org/smash/record.jsf?pid=diva2%3A1512130&dswid=4056
Mifrah S. Topic modeling coherence: A comparative study between LDA and NMF models using COVID’19 corpus. International Journal of Advanced Trends in Computer Science and Engineering. 2020;9(4):5756–5761.
Dieng AB, Ruiz F, Blei DM. Topic modeling in embedding spaces. Trans Assoc Comput Linguist. 2020;8:439–453.
Zhang F, Wang C, Trapp A, Flaherty P. A global optimization algorithm for sparse mixed membership matrix factorization. arXiv . 2016. Available from: http://arxiv.org/abs/1610.06145
Feinerer I, Hornik K, Meyer D. Text Mining Infrastructure in R. J Stat Software. 2008;25(5):1–54.
BBC Datasets. 2021. Available from: http://mlg.ucd.ie/datasets/bbc.html
Chen S, Wang Y. 2021. Available from: https://acsweb.ucsd.edu/~yuw176/report/lda.pdf

Copyright

© 2021 Parimala & Gomathi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)