Conference proceeding
InCaToMi: integrative causal topic miner between textual and non-textual time series data
Proceedings of the 21st ACM international conference on information and knowledge management, pp.2689-2691
CIKM '12
10/29/2012
DOI: 10.1145/2396761.2398727
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.
Details
- Title: Subtitle
- InCaToMi: integrative causal topic miner between textual and non-textual time series data
- Creators
- Hyun Duk KimChengXiang Zhai - University of Illinois Urbana-ChampaignThomas Rietz - University of IowaDaniel Diermeier - Northwestern UniversityMeichun Hsu - Hewlett-PackardMalu Castellanos - Hewlett-PackardCarlos Ceja Limon
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 21st ACM international conference on information and knowledge management, pp.2689-2691
- Publisher
- ACM
- Series
- CIKM '12
- DOI
- 10.1145/2396761.2398727
- Language
- English
- Date published
- 10/29/2012
- Academic Unit
- Finance
- Record Identifier
- 9984380382102771
Metrics
3 Record Views