Conference proceeding
Mining Causal Topics in Text Data: Iterative Topic Modeling with Time Series Feedback
PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), pp.885-890
01/01/2013
DOI: 10.1145/2505515.2505612
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.
Details
- Title: Subtitle
- Mining Causal Topics in Text Data: Iterative Topic Modeling with Time Series Feedback
- Creators
- Hyun Duk Kim - Univ Illinois, Dept Comp Sci, Chicago, IL 60680 USAMalu Castellanos - Hewlett-PackardMeichun Hsu - Hewlett-PackardChengXiang Zha - Univ Illinois, Dept Comp Sci, Chicago, IL 60680 USAThomas Rietz - University of IowaDaniel Diermeier - Northwestern University
- Resource Type
- Conference proceeding
- Publication Details
- PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), pp.885-890
- Publisher
- Assoc Computing Machinery
- DOI
- 10.1145/2505515.2505612
- Number of pages
- 6
- Grant note
- CNS-1027965 / National Science Foundation; National Science Foundation (NSF) HP Innovation Research Award
- Language
- English
- Date published
- 01/01/2013
- Academic Unit
- Finance
- Record Identifier
- 9984380383002771
Metrics
5 Record Views