Conference paper
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 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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2009 |
| Pages: | 1-6 |
| Proceedings: | 2009 IEEE International Workshop on Machine Learning for Signal Processing |
| Journal subtitle: | Formerly the Ieee Workshop on Neural Networks for Signal Processing |
| ISBN: | 1424449472 , 1424449480 , 9781424449477 and 9781424449484 |
| ISSN: | 21610363 and 15512541 |
| Types: | Conference paper |
| DOI: | 10.1109/MLSP.2009.5306265 |
| ORCIDs: | Mølgaard, Lasse Lohilahti and Larsen, Jan |
Councils Data analysis Data mining Informatics Inspection Music information retrieval Stability Tensile stress Text mining Wikipedia data mining global analysis method information retrieval latent variable models music music information retrieval probability stepwise probabilistic latent semantic analysis approach temporal latent semantics temporal text mining method text analysis
