Article

Discovering evolutionary theme patterns from text: an exploration of temporal text mining

Authors:

ChengXiang ZhaiAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 198 - 207

https://doi.org/10.1145/1081870.1081895

Published: 21 August 2005 Publication History

Abstract

Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news articles and revealing research trends in scientific literature. In this paper, we study a particular TTM task -- discovering and summarizing the evolutionary patterns of themes in a text stream. We define this new text mining problem and present general probabilistic methods for solving this problem through (1) discovering latent themes from text; (2) constructing an evolution graph of themes; and (3) analyzing life cycles of themes. Evaluation of the proposed methods on two different domains (i.e., news articles and literature) shows that the proposed methods can discover interesting evolutionary theme patterns effectively.

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Cited By

Zhou RWang FLiu CLu X(2025)Sparse dynamic topic model with topic birth and death over timeKnowledge and Information Systems10.1007/s10115-025-02368-8Online publication date: 23-Feb-2025
https://doi.org/10.1007/s10115-025-02368-8
Kovalchuk MFilatova AKorneev AKoreneva MNasonov DVoskresenskii ABoukhanovsky A(2024)SemConvTree: Semantic Convolutional Quadtrees for Multi-Scale Event Detection in Smart CitySmart Cities10.3390/smartcities70501077:5(2763-2780)Online publication date: 28-Sep-2024
https://doi.org/10.3390/smartcities7050107
Fang XKong L(2024)Recent Trends in Landscape Sustainability Research—A Bibliometric AssessmentLand10.3390/land1306081113:6(811)Online publication date: 6-Jun-2024
https://doi.org/10.3390/land13060811
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Index Terms

Discovering evolutionary theme patterns from text: an exploration of temporal text mining
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Copyright © 2005 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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Published: 21 August 2005

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KDD05

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KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Zhou RWang FLiu CLu X(2025)Sparse dynamic topic model with topic birth and death over timeKnowledge and Information Systems10.1007/s10115-025-02368-8Online publication date: 23-Feb-2025
https://doi.org/10.1007/s10115-025-02368-8
Kovalchuk MFilatova AKorneev AKoreneva MNasonov DVoskresenskii ABoukhanovsky A(2024)SemConvTree: Semantic Convolutional Quadtrees for Multi-Scale Event Detection in Smart CitySmart Cities10.3390/smartcities70501077:5(2763-2780)Online publication date: 28-Sep-2024
https://doi.org/10.3390/smartcities7050107
Fang XKong L(2024)Recent Trends in Landscape Sustainability Research—A Bibliometric AssessmentLand10.3390/land1306081113:6(811)Online publication date: 6-Jun-2024
https://doi.org/10.3390/land13060811
Palamarchuk DWilliams LMayer BDanielson TFaust RDeschaine LNorth C(2024)Visualizing Temporal Topic Embeddings with a CompassIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345614331:1(272-282)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456143
Nielbo KKarsdorp FWevers MLassche ABaglini RKestemont MTahmasebi N(2024)Quantitative text analysisNature Reviews Methods Primers10.1038/s43586-024-00302-w4:1Online publication date: 11-Apr-2024
https://doi.org/10.1038/s43586-024-00302-w
Kim JLee S(2023)Collective Adaptive Responses Through Coping and Sensemaking Under StressSage Open10.1177/2158244023120541813:4Online publication date: 31-Oct-2023
https://doi.org/10.1177/21582440231205418
Zhang WYao REvans RHuang WCao GShen L(2023)Collaboration of issuing agencies and topic evolution of health informatisation policies in ChinaJournal of Information Science10.1177/0165551522107432349:6(1692-1710)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1177/01655515221074323
Keith Norambuena BMitra TNorth C(2023)A Survey on Event-Based News Narrative ExtractionACM Computing Surveys10.1145/358474155:14s(1-39)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3584741
Anwar TNepal SParis CYang JWu JSheng Q(2023)Tracking the Evolution of Clusters in Social Media StreamsIEEE Transactions on Big Data10.1109/TBDATA.2022.32042079:2(701-715)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3204207
Ma JWang LZhang YYuan WGuo W(2023)An integrated latent Dirichlet allocation and Word2vec method for generating the topic evolution of mental models from global to localExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118695212:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.118695
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