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

Hyun Duk Kim, Malu Castellanos, Meichun Hsu, Cheng Xiang Zhai, Thomas Rietz, Daniel Diermeier

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. Copyright is held by the owner/author(s).

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages885-890
Number of pages6
DOIs
StatePublished - Dec 11 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period10/27/1311/1/13

Keywords

  • Causal topic mining
  • Iterative topic mining
  • Time series

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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