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Content Provider | ACM Digital Library |
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Author | Diermeier, Daniel Hsu, Meichun Zhai, ChengXiang Kim, Hyun Duk Rietz, Thomas Castellanos, Malu |
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. |
Starting Page | 885 |
Ending Page | 890 |
Page Count | 6 |
File Format | |
ISBN | 9781450322638 |
DOI | 10.1145/2505515.2505612 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2013-10-27 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Iterative topic mining Causal topic mining Time series |
Content Type | Text |
Resource Type | Article |
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