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

Hyun Duk Kim, Cheng Xiang Zhai, Thomas A. Rietz, Daniel Diermeier, Meichun Hsu, Malu Castellanos, Carlos A. Ceja Limon

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

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.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages2689-2691
Number of pages3
DOIs
StatePublished - Dec 19 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1211/2/12

Keywords

  • causal topic mining
  • integrative topic mining
  • time series

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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