Temporal analysis of text data using latent variable models

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2009



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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.
Original languageEnglish
Title of host publication2009 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. : Formerly the IEEE Workshop on Neural Networks for Signal Processing
Publication date2009
ISBN (print)978-1-4244-4947-7
StatePublished - 2009
Event2009 IEEE International Workshop on Machine Learning for Signal Processing - Grenoble, France


Workshop2009 IEEE International Workshop on Machine Learning for Signal Processing
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