Cited By
View all- Ford JLeeds JKubosumi S(2024)Mission Statement Topic Models: Coherence, Diversity, and UtilityDigital Humanities Looking at the World10.1007/978-3-031-48941-9_12(153-166)Online publication date: 20-Apr-2024
Objective Functions to Determine the Number of Topics for Topic Modeling
Authors: 
Silvio Peikert, Clemens Kubach, Jamal Al Qundus, Le Duyen Sandra Vu, Adrian PaschkeAuthors Info & Claims
Silvio Peikert, Clemens Kubach, Jamal Al Qundus, Le Duyen Sandra Vu, Adrian PaschkeAuthors Info & ClaimsPages 328 - 332
Abstract
Topic modeling is a well-known task in unsupervised machine learning, where clustering algorithms are used to find latent topics. Several algorithms are presented in the literature, but the best known of them suffer from the drawback of requiring a lot of hyperparameter tuning to achieve good results. Especially, the number of latent topics or clusters (k) needs to be known in advance. In view of this situation, this paper analyses objective functions that help to evaluate the models in order to determine optimal hyperparameters. An empirical qualitative study was conducted using the NMF algorithm on different datasets to experimentally determine numerical properties of topic models which indicate an optimal k. Based on this study, we propose objective functions to select optimal topic models and discuss their results on different datasets.
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November 2021
658 pages
ISBN:9781450395564
DOI:10.1145/3487664
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Published: 30 December 2021
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iiWAS2021
iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence
November 29 - December 1, 2021
Linz, Austria
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Cited By
View all- Ford JLeeds JKubosumi S(2024)Mission Statement Topic Models: Coherence, Diversity, and UtilityDigital Humanities Looking at the World10.1007/978-3-031-48941-9_12(153-166)Online publication date: 20-Apr-2024
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