Article

Dynamic topic models

Authors:

John D. LaffertyAuthors Info & Claims

ICML '06: Proceedings of the 23rd international conference on Machine learning

Pages 113 - 120

https://doi.org/10.1145/1143844.1143859

Published: 25 June 2006 Publication History

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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Cited By

LI XKAWAMURA KKIMURA Y(2024)Quantifying the Activities of Local Assembly Members in Japan: Recent Advances and an Approach Using the BERTopic ModelInterdisciplinary Information Sciences10.4036/iis.2024.R.0330:1(68-101)Online publication date: 2024
https://doi.org/10.4036/iis.2024.R.03
Figuera PGarcía Bringas P(2024)Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and InsightsTechnologies10.3390/technologies1201000512:1(5)Online publication date: 3-Jan-2024
https://doi.org/10.3390/technologies12010005
Moore TCardamone NVonVille HCoulter R(2024)The Integration of Complex Systems Science and Community-Based Research: A Scoping ReviewSystems10.3390/systems1203008812:3(88)Online publication date: 9-Mar-2024
https://doi.org/10.3390/systems12030088
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Index Terms

Dynamic topic models
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Canonical correlation analysis
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Regression analysis
      2. Statistical graphics

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cover image ACM Other conferences

ICML '06: Proceedings of the 23rd international conference on Machine learning

June 2006

1154 pages

ISBN:1595933832

DOI:10.1145/1143844

Program Chairs:
William Cohen,
Andrew Moore

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2006

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ICML '06 Paper Acceptance Rate 140 of 548 submissions, 26%;

Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

LI XKAWAMURA KKIMURA Y(2024)Quantifying the Activities of Local Assembly Members in Japan: Recent Advances and an Approach Using the BERTopic ModelInterdisciplinary Information Sciences10.4036/iis.2024.R.0330:1(68-101)Online publication date: 2024
https://doi.org/10.4036/iis.2024.R.03
Figuera PGarcía Bringas P(2024)Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and InsightsTechnologies10.3390/technologies1201000512:1(5)Online publication date: 3-Jan-2024
https://doi.org/10.3390/technologies12010005
Moore TCardamone NVonVille HCoulter R(2024)The Integration of Complex Systems Science and Community-Based Research: A Scoping ReviewSystems10.3390/systems1203008812:3(88)Online publication date: 9-Mar-2024
https://doi.org/10.3390/systems12030088
Chen HWu YTang HLei JWang GZhao WLiao JWang FWang Z(2024)Visual Analysis Method for Traffic Trajectory with Dynamic Topic Movement Patterns Based on the Improved Markov Decision ProcessElectronics10.3390/electronics1303046713:3(467)Online publication date: 23-Jan-2024
https://doi.org/10.3390/electronics13030467
Wang TXu HLi CZhang FWang J(2024)Dynamic insights into research trends and trajectories in early reading: an analytical exploration via dynamic topic modelingFrontiers in Psychology10.3389/fpsyg.2024.132649415Online publication date: 7-Feb-2024
https://doi.org/10.3389/fpsyg.2024.1326494
Kassab LKryshchenko ALyu HMolitor DNeedell DRebrova EYuan J(2024)Sparseness-constrained nonnegative tensor factorization for detecting topics at different time scalesFrontiers in Applied Mathematics and Statistics10.3389/fams.2024.128707410Online publication date: 22-Jul-2024
https://doi.org/10.3389/fams.2024.1287074
Coccia MRoshani S(2024)NEW DIRECTIONS IN QUANTUM COMPUTING TECHNOLOGYSSRN Electronic Journal10.2139/ssrn.4871417Online publication date: 2024
https://doi.org/10.2139/ssrn.4871417
Tang ZPan XGu Z(2024)Analyzing public demands on China’s online government inquiry platform: A BERTopic-Based topic modeling studyPLOS ONE10.1371/journal.pone.029685519:2(e0296855)Online publication date: 15-Feb-2024
https://doi.org/10.1371/journal.pone.0296855
Shiu SLehti-Shiu M(2024)Assessing the evolution of research topics in a biological field using plant science as an examplePLOS Biology10.1371/journal.pbio.300261222:5(e3002612)Online publication date: 23-May-2024
https://doi.org/10.1371/journal.pbio.3002612
Zhuo XIrresberger FBostandzic D(2024)How are texts analyzed in blockchain research? A systematic literature reviewFinancial Innovation10.1186/s40854-023-00501-610:1Online publication date: 29-Feb-2024
https://doi.org/10.1186/s40854-023-00501-6
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