document: 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 generalize some of these models proposed in the previous work and 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 the proposed mixture model can discover themes and their contextual variations effectively .
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