document: Topics over time : a non-Markov continuous-time model of topical trends This paper presents an LDA-style topic model that captures not only the low-dimensional structure of data , but also how the structure changes over time . Unlike other recent work that relies on Markov assumptions or discretization of time , here each topic is associated with a continuous distribution over timestamps , and for each generated document , the mixture distribution over topics is influenced by both word co-occurrences and the document 's timestamp . Thus , the meaning of a particular topic can be relied upon as constant , but the topics ' occurrence and correlations change significantly over time . We present results on nine months of personal email , 17 years of NIPS research papers and over 200 years of presidential state-of-the-union addresses , showing improved topics , better timestamp prediction , and interpretable trends .
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