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dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorFu, Qen_US
dc.creatorZhuang, Yen_US
dc.creatorGu, Jen_US
dc.creatorZhu, Yen_US
dc.creatorQin, Hen_US
dc.creatorGuo, Xen_US
dc.identifier.isbn978-1-7281-0858-2 (Electronic)en_US
dc.identifier.isbn978-1-7281-0857-5 (USB)en_US
dc.identifier.isbn978-1-7281-0859-9 (Print on Demand(PoD))en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication Q. Fu, Y. Zhuang, J. Gu, Y. Zhu, H. Qin and X. Guo, "Search for K: Assessing Five Topic-Modeling Approaches to 120,000 Canadian Articles," 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 3640-3647 is available at
dc.subjectTopic modelingen_US
dc.subjectNatural language processingen_US
dc.subjectSocial scienceen_US
dc.subjectOptimal number of topicsen_US
dc.titleSearch for K : assessing five topic-modeling approaches to 120,000 Canadian articlesen_US
dc.typeConference Paperen_US
dcterms.abstractTopic modeling has been an important field in natural language processing (NLP) and recently witnessed great methodological advances. Yet, the development of topic modeling is still, if not increasingly, challenged by two critical issues. First, despite intense efforts toward nonparametric/post-training methods, the search for the optimal number of topics K remains a fundamental question in topic modeling and warrants input from domain experts. Second, with the development of more sophisticated models, topic modeling is now ironically been treated as a black box and it becomes increasingly difficult to tell how research findings are informed by data, model specifications, or inference algorithms. Based on about 120,000 newspaper articles retrieved from three major Canadian newspapers (Globe and Mail, Toronto Star, and National Post) since 1977, we employ five methods with different model specifications and inference algorithms (Latent Semantic Analysis, Latent Dirichlet Allocation, Principal Component Analysis, Factor Analysis, Nonnegative Matrix Factorization) to identify discussion topics. The optimal topics are then assessed using three measures: coherence statistics, held-out likelihood (loss), and graph-based dimensionality selection. Mixed findings from this research complement advances in topic modeling and provide insights into the choice of optimal topics in social science research.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, p. 3640-3647en_US
dc.relation.conferenceIEEE International Conference on Big Data (Big Data)en_US
dc.description.validate202009 bcrcen_US
dc.description.oaAccepted Manuscripten_US
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