Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
Title: Agreeing to disagree : choosing among eight topic-modeling methods
Authors: Fu, Q
Zhuang, Y
Gu, J
Zhu, Y
Guo, X 
Issue Date: 15-Feb-2021
Source: Big data research, 15 Feb. 2021, v. 23, 100173
Abstract: Topic modeling is a key research area in natural language processing and has inspired innovative studies in a wide array of social-science disciplines. Yet, the use of topic modeling in computational social science has been hampered by two critical issues. First, social scientists tend to focus on a few standard ways of topic modeling. Our understanding of semantic patterns has not been informed by rapid methodological advances in topic modeling. Moreover, a systematic comparison of the performance of different methods in this field is warranted. Second, the choice of the optimal number of topics remains a challenging task. A comparison of topic-modeling techniques has rarely been situated in a social-science context and the choice appears to be arbitrary for most social scientists. Based on about 120,000 Canadian newspaper articles since 1977, we review and compare eight traditional, generative, and neural methods for topic modeling (Latent Semantic Analysis, Principal Component Analysis, Factor Analysis, Non-negative Matrix Factorization, Latent Dirichlet Allocation, Neural Autoregressive Topic Model, Neural Variational Document Model, and Hierarchical Dirichlet Process). Three measures (coherence statistics, held-out likelihood, and graph-based dimensionality selection) are then used to assess the performance of these methods. Findings are presented and discussed to guide the choice of topic-modeling methods, especially in social science research.
Keywords: Topic modeling
Natural language processing
Computational social science
Optimal number of topics
Publisher: Elsevier
Journal: Big data research 
ISSN: 2214-5796
EISSN: 2214-580X
DOI: 10.1016/j.bdr.2020.100173
Rights: ©2020 Elsevier Inc. All rights reserved.
© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
The following publication Fu, Q., Zhuang, Y., Gu, J., Zhu, Y., & Guo, X. (2021). Agreeing to Disagree: Choosing Among Eight Topic-Modeling Methods. Big Data Research, 23, 100173 is available at
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Fu_Agreeing_Disagree_Topic-Modeling.pdfPre-Published version1.19 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

Last Week
Last month
Citations as of Sep 17, 2023


Citations as of Sep 17, 2023


Citations as of Sep 21, 2023


Citations as of Sep 21, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.