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                            [identifier] => oai:ojs2.ijsmc.pro-metrics.org:article/349
                            [datestamp] => 2026-03-30T11:22:09Z
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                                    [title] => Benchmarking nonnegative tensor factorization for topic modeling: Coherence, separability, and cost
                                    [creator] => Array
                                        (
                                            [0] => Grisales-Aguirre, Andres
                                            [1] => Robledo, Sebastian
                                            [2] =>  Eggers, Fabian
                                        )

                                    [subject] => Array
                                        (
                                            [0] => natural language processing
                                            [1] => topic modeling
                                            [2] => nonnegative tensor factorization
                                            [3] => term entropy
                                            [4] => machine learning
                                            [5] => unstructured data analysis
                                        )

                                    [description] => Objective. The objective of this study was to determine whether nonnegative tensor factorization (NTF) could enhance topic modeling (TM) in comparison with conventional matrix-based methodologies (latent semantic indexing/latent semantic analysis, latent Dirichlet allocation [LDA], and nonnegative matrix factorization). Furthermore, it aimed to test the hypothesis that a multiway representation could better preserve higher-order term–document structure than two-dimensional document–term matrices.
Design/Methodology/Approach. A third-order document representation was constructed, incorporating term entropy as an informativeness mode in conjunction with local weighting (tf-idf ). The resulting tensor was factorized using an NTF procedure based on alternating least squares with nonnegativity constraints and regularization. The performance of the model was evaluated using several metrics. These included topic coherence Cv, qualitative interpretability diagnostics (such as word clouds, topic-similarity heat maps, and t-SNE), and computational cost (measured in training time).
Results/Discussion. In a supervised classification setting, cross-validation yielded strong performance for NTF (F1, precision = 0.98, recall = 0.97, accuracy = 0.93) with high coherence (Cv = 0.87). In an unsupervised benchmark aligned with Singh et al. (2023), a coherence-based topic selection method identified an optimal selection of eight topics. Furthermore, the NTF approach yielded higher average coherence than the reference LDA-based results (0.52 vs. 0.47). These gains, however, were accompanied by an augmented computational cost, as evidenced by the extended training time required (e.g., 39 seconds).
Conclusions. Entropy-augmented tensor representations in combination with NTF yielded more coherent and distinctly separated topics than matrix-based baselines, albeit with elevated computational demands. Moreover, these findings suggested that the proposed NTF approach could be highly valuable for applications such as science mapping studies, academic literature analysis, and systematic reviews.
Originality/Value. The study empirically validated a term-entropy–augmented tensor construction for TM and demonstrated that preserving multiway structure could improve topic coherence and separability beyond conventional two-dimensional document–term representations.
                                    [publisher] => Pro-Metrics
                                    [date] => 2026-03-30
                                    [type] => Array
                                        (
                                            [0] => info:eu-repo/semantics/article
                                            [1] => info:eu-repo/semantics/publishedVersion
                                            [2] => Peer-reviewed article
                                        )

                                    [format] => application/pdf
                                    [identifier] => Array
                                        (
                                            [0] => https://ijsmc.pro-metrics.org/index.php/i/article/view/349
                                            [1] => 10.47909/ijsmc.349
                                        )

                                    [source] => Array
                                        (
                                            [0] => Iberoamerican Journal of Science Measurement and Communication; Vol. 6 (2026): Forthcoming articles; 1-18
                                            [1] => Iberoamerican Journal of Science Measurement and Communication; Vol. 6 (2026): Próximos artículos; 1-18
                                            [2] => Iberoamerican Journal of Science Measurement and Communication; Vol. 6 (2026): Artigos futuros; 1-18
                                            [3] => 2709-3158
                                            [4] => 2709-7595
                                        )

                                    [language] => eng
                                    [relation] => https://ijsmc.pro-metrics.org/index.php/i/article/view/349/209
                                    [rights] => Array
                                        (
                                            [0] => Copyright (c) 2026 Andres Grisales-Aguirre, Sebastian Robledo, Fabian  Eggers
                                            [1] => https://creativecommons.org/licenses/by-nc/4.0
                                        )

                                )

                        )

                )

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)