Temporal analysis of text data using latent variable models

AbstractDetecting 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.
TypeConference paper [With referee]
Conference2009 IEEE International Workshop on Machine Learning for Signal Processing
Year2009    Month September
ISBN / ISSNISBN 9781424449477
NoteDOI 10.1109/MLSP.2009.5306262
Electronic version(s)[pdf]
Publication linkhttp://mlsp2009.conwiz.dk
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing