Name
Title | ||
|---|---|---|
Mining causal topics in text data: iterative topic modeling with time series feedback |
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2505515.2505612 | CIKM |
Keywords | Field | DocType |
time series feedback,time series,mining causal topic,prior distribution,coherent semantically,novel general text mining,proposed framework,external time series variable,iterative topic modeling,text data,time-series causal analysis,time series data,causal topic | Causal analysis,Time series,Data mining,Text mining,Information retrieval,Computer science,Probabilistic logic,Topic model | Conference |
Citations | PageRank | References |
11 | 0.64 | 14 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyun Duk Kim | 1 | 157 | 8.05 |
| Malú Castellanos | 2 | 857 | 54.71 |
| Meichun Hsu | 3 | 3437 | 778.34 |
| ChengXiang Zhai | 4 | 11908 | 649.74 |
| Thomas Rietz | 5 | 11 | 0.64 |
| Daniel Diermeier | 6 | 58 | 12.45 |