A mixture model for contextual text mining

14 years 14 days ago
A mixture model for contextual text mining
Contextual text mining is concerned with extracting topical themes from a text collection with context information (e.g., time and location) and comparing/analyzing the variations of themes over different contexts. Since the topics covered in a document are usually related to the context of the document, analyzing topical themes within context can potentially reveal many interesting theme patterns. In this paper, we propose a new general probabilistic model for contextual text mining that can cover several existing models as special cases. Specifically, we extend the probabilistic latent semantic analysis (PLSA) model by introducing context variables to model the context of a document. The proposed mixture model, called contextual probabilistic latent semantic analysis (CPLSA) model, can be applied to many interesting mining tasks, such as temporal text mining, spatiotemporal text mining, author-topic analysis, and cross-collection comparative analysis. Empirical experiments show that...
Qiaozhu Mei, ChengXiang Zhai
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2006
Where KDD
Authors Qiaozhu Mei, ChengXiang Zhai
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