document: Discovering evolutionary theme patterns from text : an exploration of temporal text mining Temporal Text Mining ( TTM ) is concerned with discovering temporal patterns in text information collected over time . Since most text information bears some time stamps , TTM has many applications in multiple domains , such as summarizing events in news articles and revealing research trends in scientific literature . In this paper , we study a particular TTM task -- discovering and summarizing the evolutionary patterns of themes in a text stream . We define this new text mining problem and present general probabilistic methods for solving this problem through ( 1 ) discovering latent themes from text ; ( 2 ) constructing an evolution graph of themes ; and ( 3 ) analyzing life cycles of themes . Evaluation of the proposed methods on two different domains ( i.e. , news articles and literature ) shows that the proposed methods can discover interesting evolutionary theme patterns effectively .
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