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ldagibbs: A command for topic modeling in Stata using latent Dirichlet allocation

Author

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  • Carlo Schwarz

    (University of Warwick)

Abstract

In this article, I introduce the ldagibbs command, which implements latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most popular machine-learning topic model. Topic models automatically cluster text documents into a user-chosen number of topics. Latent Dirichlet allocation represents each document as a probability distribution over topics and represents each topic as a probability distribution over words. Therefore, latent Dirichlet allocation provides a way to analyze the content of large unclassified text data and an alternative to predefined document classifications.

Suggested Citation

  • Carlo Schwarz, 2018. "ldagibbs: A command for topic modeling in Stata using latent Dirichlet allocation," Stata Journal, StataCorp LP, vol. 18(1), pages 101-117, March.
    • Handle: RePEc:tsj:stataj:v:18:y:2018:i:1:p:101-117
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    Citations

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    Cited by:

    1. McCannon, Bryan & Zhou, Yang & Hall, Joshua, 2021. "Measuring a Contract’s Breadth: A Text Analysis," Working Papers 11013, George Mason University, Mercatus Center.
    2. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    3. Julio Elias & Nicola Lacetera & Mario Macis, 2022. "Is the Price Right? The Role of Morals, Ideology, and Tradeoff Thinking in Explaining Reactions to Price Surges," CESifo Working Paper Series 9712, CESifo.
    4. Leonardo Bursztyn & Aakaash Rao & Christopher Roth & David Yanagizawa-Drott, 2023. "Opinions as Facts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1832-1864.
    5. Hector Carcel-Villanova, 2022. "Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    6. Elisa Facchetti & Lorenzo Neri & Marco Ovidi, 2021. "Should you Meet The Parents? The impact of information on non-test score attributes on school choice," DISCE - Working Papers del Dipartimento di Economia e Finanza def113, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    7. Bryan McCannon & Joshua Hall & Yang Zhou, 2023. "Measuring a contract's breadth: A text analysis," American Journal of Economics and Sociology, Wiley Blackwell, vol. 82(1), pages 5-14, January.
    8. Christopher Rauh & Laëtitia Renée, 2023. "How to measure parenting styles?," Review of Economics of the Household, Springer, vol. 21(3), pages 1063-1081, September.
    9. Rongqin Liu & Yun Zhang & Chuan Luo & Shangyu Tan & Yunqu Gong, 2024. "Review content type and hotel review helpfulness: direct and moderating effects," Information Technology and Management, Springer, vol. 25(4), pages 383-406, December.
    10. Yuexia Han & Zhuang Chen & Yuxin Hu & Liyan Zhang & Huishan Fu & Renyong Zhang & Wei Zhang, 2023. "A PIE analysis of China’s commercial space development," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    11. Damane Moeti, 2022. "Topic Classification of Central Bank Monetary Policy Statements: Evidence from Latent Dirichlet Allocation in Lesotho," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 10(1), pages 199-227, September.
    12. Ali Sina Önder & Sergey V. Popov & Sascha Schweitzer, 2021. "Leadership in Scholarship: Editors’ Appointments and the Profession’s Narrative," Working Papers in Economics & Finance 2021-05, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.

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