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

Authors:
Hyun Duk Kim;Malu Castellanos;Meichun Hsu;ChengXiang Zhai;Thomas Rietz;Daniel Diermeier
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL, USA;HP Laboratories, Palo Alto, CA, USA;HP Laboratories, Palo Alto, CA, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA;The University of Iowa, Iowa City, IA, USA;Northwestern University, Evanston, IL, USA
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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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.