Optimal Number of Topics in Topic Modeling Using Deep Auto Encoder Graph Regularized Non-Negative Matrix Factorization Algorithm

Topic modeling stands as a well-explored and foundational challenge in the text mining domain. Traditional topic schemes based on word co-occurrences, aim to expose the latent semantic structure embedded in a document corpus. Nevertheless, the inherent brevity of short texts introduces data sparsity...

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Bibliographic Details
Published in:	Journal of systems science and systems engineering Vol. 34; no. 3; pp. 257 - 283
Main Authors:	Kherwa, Pooja, Arora, Jyoti
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025 Springer Nature B.V
Subjects:	Algorithms Coders Complexity Data mining Datasets Documents Economic Theory/Quantitative Economics/Mathematical Methods Engineering Factorization Feature extraction Modelling Natural language processing Operations Research/Decision Theory Preprocessing Semantics Texts Words (language) Topic modeling purity and topic coherence natural language processing non-negative matrix factorization
ISSN:	1004-3756, 1861-9576
Online Access:	Get full text
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