Temporal analysis of text data using latent variable models

Detecting and tracking of temporal data is an important task in multiple applications. In this paper we study temporal text mining methods for music information retrieval. We compare two ways of detecting the temporal latent semantics of a corpus extracted from Wikipedia, using a stepwise probabilis...

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Bibliographic Details
Main Authors: Molgaard, L.L., Larsen, J., Goutte, C.
Format: Conference Proceeding
Language:English
Subjects:
Councils
Data analysis
Data mining
Informatics
Inspection
Music information retrieval
Stability
Tensile stress
Text mining
Wikipedia
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Summary:Detecting and tracking of temporal data is an important task in multiple applications. In this paper we study temporal text mining methods for music information retrieval. We compare two ways of detecting the temporal latent semantics of a corpus extracted from Wikipedia, using a stepwise probabilistic latent semantic analysis (PLSA) approach and a global multiway PLSA method. The analysis indicates that the global analysis method is able to identify relevant trends which are difficult to get using a step-by-step approach. Furthermore we show that inspection of PLSA models with different number of factors may reveal the stability of temporal clusters making it possible to choose the relevant number of factors.
ISSN:1551-2541
2378-928X
DOI:10.1109/MLSP.2009.5306265