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DYNAMIC TOPIC MODELS
* Authors:
* David M. Blei
Princeton University, Princeton, NJ
Princeton University, Princeton, NJ
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* John D. Lafferty
Carnegie Mellon University, Pittsburgh PA
Carnegie Mellon University, Pittsburgh PA
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ICML '06: Proceedings of the 23rd international conference on Machine
learningJune 2006Pages 113–120https://doi.org/10.1145/1143844.1143859
Published:25 June 2006Publication History
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ICML '06: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON MACHINE LEARNING
Dynamic topic models
Pages 113–120
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* * ABSTRACT
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ICML '06: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON MACHINE LEARNING
Dynamic topic models
Pages 113–120
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ABSTRACT
A family of probabilistic time series models is developed to analyze the time
evolution of topics in large document collections. The approach is to use state
space models on the natural parameters of the multinomial distributions that
represent the topics. Variational approximations based on Kalman filters and
nonparametric wavelet regression are developed to carry out approximate
posterior inference over the latent topics. In addition to giving quantitative,
predictive models of a sequential corpus, dynamic topic models provide a
qualitative window into the contents of a large document collection. The models
are demonstrated by analyzing the OCR'ed archives of the journal Science from
1880 through 2000.
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Show All References
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1. Moral P. (2023). A tale of heroes and villains: Russia’s strategic
narratives on Twitter during the COVID-19 pandemic. Journal of
Information Technology & Politics. 10.1080/19331681.2023.2182862. 21:2.
(146-165). Online publication date: 2-Apr-2024.
https://www.tandfonline.com/doi/full/10.1080/19331681.2023.2182862
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Algorithm for Topic-Specific Analysis of Sentences. Social Science
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publication date: 1-Feb-2024.
http://journals.sagepub.com/doi/10.1177/08944393231178605
3. Schwiderowski J, Pedersen A and Beck R. (2023). Crypto Tokens and Token
Systems. Information Systems Frontiers. 10.1007/s10796-023-10382-w. 26:1.
(319-332). Online publication date: 1-Feb-2024.
https://link.springer.com/10.1007/s10796-023-10382-w
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technology trends in scientific publications: a graph propagated neural
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Forecasting With Big Data: A Systematic Review of the Literature. Journal
of Hospitality & Tourism Research. 10.1177/10963480231223151.
http://journals.sagepub.com/doi/10.1177/10963480231223151
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methods, applications, and challenges. Artificial Intelligence Review.
10.1007/s10462-023-10661-7. 57:2.
https://link.springer.com/10.1007/s10462-023-10661-7
7. Chen H, Wu Y, Tang H, Lei J, Wang G, Zhao W, Liao J, Wang F and Wang Z.
(2024). Visual Analysis Method for Traffic Trajectory with Dynamic Topic
Movement Patterns Based on the Improved Markov Decision Process.
Electronics. 10.3390/electronics13030467. 13:3. (467).
https://www.mdpi.com/2079-9292/13/3/467
8. Figuera P and García Bringas P. (2024). Revisiting Probabilistic Latent
Semantic Analysis: Extensions, Challenges and Insights. Technologies.
10.3390/technologies12010005. 12:1. (5).
https://www.mdpi.com/2227-7080/12/1/5
9. Gao X, Jolly E, Yu H, Liu H, Zhou X and Chang L. (2024). The
psychological, computational, and neural foundations of indebtedness.
Nature Communications. 10.1038/s41467-023-44286-9. 15:1.
https://www.nature.com/articles/s41467-023-44286-9
10. Kumar A, Bala P, Chakraborty S and Behera R. (2024). Exploring
antecedents impacting user satisfaction with voice assistant app: A text
mining-based analysis on Alexa services. Journal of Retailing and
Consumer Services. 10.1016/j.jretconser.2023.103586. 76. (103586). Online
publication date: 1-Jan-2024.
https://linkinghub.elsevier.com/retrieve/pii/S0969698923003375
11. Keith Norambuena B, Mitra T and North C. (2023). A Survey on Event-Based
News Narrative Extraction. ACM Computing Surveys. 55:14s. (1-39). Online
publication date: 31-Dec-2024.
https://doi.org/10.1145/3584741
12. Alfaqeeh M and Skillicorn D. (2023). Community detection in social
networks by spectral embedding of typed graphs. Social Network Analysis
and Mining. 10.1007/s13278-023-01172-y. 14:1.
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Large Language Models for Topic Modeling 2023 IEEE International
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China’s BIM technology policy based on quantitative mapping.
Architectural Engineering and Design Management.
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18. Ghazinoori S, Roshani S, Hafezi R and Wood D. (2023). Bursting into the
Public Eye: Analyzing the Development of Renewable Energy Research
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20. Laureate C, Buntine W and Linger H. (2023). A systematic review of the
use of topic models for short text social media analysis. Artificial
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INDEX TERMS
1. DYNAMIC TOPIC MODELS
1. Computing methodologies
1. Machine learning
1. Machine learning approaches
1. Factorization methods
1. Canonical correlation analysis
2. Mathematics of computing
1. Probability and statistics
1. Statistical paradigms
1. Regression analysis
2. Statistical graphics
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