Finding scientific topics
Open accessAbstract
A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying “hot topics” by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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Removing all words that do not appear in at least five websites ensures that misspellings, addresses and enterprise names are removed from the dataset.
“We also removed all words that do not appear in at least five websites (Griffiths and Steyvers, 2004); doing this ensures that misspellings, addresses and enterprise names are removed from the dataset, as well as some remaining foreign words.”
From Sustainability startups and where to find them: Investigating the share of sustainability startups across entrepreneurial ecosystems and the causal drivers of differences (2021) · cites this paper as Griffiths and Steyvers, 2004
In “How Many Topics?” (page unavailable), Griffiths and Steyvers report deleting words that occurred in fewer than five PNAS abstracts, which supports the citing paper’s use of a five-document threshold. The source studies abstracts, not websites, and does not say this threshold ensures removal of misspellings, addresses, enterprise names, or foreign words. (LLM, conf=0.88)
Experimental — accuracy not yet measuredPerplexity measures uncertainty in predicting word appearance.
“I calculate the uncertainty in predicting each word appearance in every text through perplexity (Griffiths & Steyvers, 2004)”
From The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions (2022) · cites this paper as Griffiths & Steyvers, 2004
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- Authors
- Griffiths, T. L., & Steyvers, M.
- Affiliations
- University of California, Irvine (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2004-04-06
- Volume
- 101
- Issue
- suppl_1
- Pages
- 5228-5235
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC387300
- OpenAlex
- W2001082470
- Cited by
- 6,049
- References
- 12
- Citations by year
- 2026: 233 · 2025: 291 · 2024: 302 · 2023: 311 · 2022: 330 · 2021: 400 · 2020: 384 · 2019: 419 · 2018: 400 · 2017: 381 · 2016: 371 · 2015: 396 · 2014: 399 · 2013: 318 · 2012: 280
- FWCI
- 39.79
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- topic modeling, Markov chain Monte Carlo inference, Bayesian model selection, scientific abstracts, hot topics, temporal dynamics, semantic annotation, scientific literature analysis
- MeSH terms
- Documentation, Monte Carlo Method, National Academy of Sciences, U.S., Probability, Publishing, Science, United States, Models, Statistical, Databases, Factual
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