InCaToMi: integrative causal topic miner between textual and non-textual time series data

Authors:
Hyun Duk Kim;ChengXiang Zhai;Thomas A. Rietz;Daniel Diermeier;Meichun Hsu;Malu Castellanos;Carlos A. Ceja Limon
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA;The University of Iowa, Iowa City, IA, USA;Northwestern University, Evanston, IL, USA;HP Laboratories, Palo Alto, CA, USA;HP Laboratories, Palo Alto, CA, USA;HP Laboratories, Guadalajara, Mexico
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

Topic modeling is popular for text mining tasks. Recently, topic modeling has been combined with time lines when textual data is related to external non-textual time series data such as stock prices. However, no previous work has used the external non-textual time series data in the process of topic modeling. In this paper, we describe a novel text mining system, Integrative Causal Topic Miner (InCaToMi) that integrates textual and non-textual time series data. InCaToMi automatically finds causal relationships and topics using text data and external non-textual time series data using Granger Testing. Moreover, InCaToMi considers the non-textual time series data in the topic modeling process, using the time series data to iteratively improve modeling results through interactions between it and the textual data at both topic and word levels.