research-article

Mining causal topics in text data: iterative topic modeling with time series feedback

Authors:

Malu Castellanos,

ChengXiang Zhai,

Daniel DiermeierAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 885 - 890

https://doi.org/10.1145/2505515.2505612

Published: 27 October 2013 Publication History

Abstract

Many applications require analyzing textual topics in conjunction with external time series variables such as stock prices. We develop a novel general text mining framework for discovering such causal topics from text. Our framework naturally combines any given probabilistic topic model with time-series causal analysis to discover topics that are both coherent semantically and correlated with time series data. We iteratively refine topics, increasing the correlation of discovered topics with the time series. Time series data provides feedback at each iteration by imposing prior distributions on parameters. Experimental results show that the proposed framework is effective.

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Cited By

Li YYang AMarelli ALi Y(2024)MixEHR-SurGJournal of Biomedical Informatics10.1016/j.jbi.2024.104638153:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.jbi.2024.104638
Wan WChen YGao YShao CZhao Y(2023)A fine-grained causality extraction model incorporating relative location codingApplied Intelligence10.1007/s10489-023-04970-153:22(27163-27176)Online publication date: 2-Sep-2023
https://dl.acm.org/doi/10.1007/s10489-023-04970-1
Riabykh ASurzhko DKonovalikhin MKoltcov S(2022)STTM: an efficient approach to estimating news impact on stock movement directionPeerJ Computer Science10.7717/peerj-cs.11568(e1156)Online publication date: 16-Dec-2022
https://doi.org/10.7717/peerj-cs.1156
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Index Terms

Mining causal topics in text data: iterative topic modeling with time series feedback
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013.

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 the author(s) 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: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Li YYang AMarelli ALi Y(2024)MixEHR-SurGJournal of Biomedical Informatics10.1016/j.jbi.2024.104638153:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.jbi.2024.104638
Wan WChen YGao YShao CZhao Y(2023)A fine-grained causality extraction model incorporating relative location codingApplied Intelligence10.1007/s10489-023-04970-153:22(27163-27176)Online publication date: 2-Sep-2023
https://dl.acm.org/doi/10.1007/s10489-023-04970-1
Riabykh ASurzhko DKonovalikhin MKoltcov S(2022)STTM: an efficient approach to estimating news impact on stock movement directionPeerJ Computer Science10.7717/peerj-cs.11568(e1156)Online publication date: 16-Dec-2022
https://doi.org/10.7717/peerj-cs.1156
Shirdel MSegata MDi Fatta GLiotta A(2022)Event Detection in Financial Markets2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927759(1-8)Online publication date: 12-Sep-2022
https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927759
Cendrero JGonzalo JGalletero MZapata I(2022)Time Series Impact Through Topic ModelingIEEE Access10.1109/ACCESS.2022.320296010(97327-97347)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3202960
Drury BGonçalo Oliveira Hde Andrade Lopes A(2022)A survey of the extraction and applications of causal relationsNatural Language Engineering10.1017/S135132492100036X28:3(361-400)Online publication date: 20-Jan-2022
https://doi.org/10.1017/S135132492100036X
Yang JHan SPoon J(2022)A survey on extraction of causal relations from natural language textKnowledge and Information Systems10.1007/s10115-022-01665-w64:5(1161-1186)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10115-022-01665-w
Gopal GKovoor BMini U(2020)Keyword Template Based Semi-supervised Topic Modelling in TweetsInternational Conference on Innovative Computing and Communications10.1007/978-981-15-5148-2_58(659-666)Online publication date: 31-Jul-2020
https://doi.org/10.1007/978-981-15-5148-2_58
Berenberg DBagrow J(2018)Efficient Crowd Exploration of Large NetworksProceedings of the ACM on Human-Computer Interaction10.1145/32742932:CSCW(1-25)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1145/3274293
Chen KJi XWang H(2017)A search index-enhanced feature model for news recommendationJournal of Information Science10.1177/016555151663980143:3(328-341)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1177/0165551516639801
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