Theme chronicle model: chronicle consists of timestamp and topical words over each theme

This paper presents a topic model that discovers the correlation patterns in a given time-stamped document collection and how these patterns evolve over time. Our proposal, the theme chronicle model (TCM) divides traditional topics into temporal and stable topics to detect the change of each theme over time; previous topic models ignore these differences and characterize trends as merely bursts of topics. TCM introduces a theme topic (stable topic), a trend topic (temporal topic), timestamps, and a latent switch variable in each token to realize these differences. Its topic layers allow TCM to capture not only word co-occurrence patterns in each theme, but also word co-occurrence patterns at any given time in each theme as trends. Experiments on various data sets show that the proposed model is useful as a generative model to discover fine-grained tightly coherent topics, takes advantage of previous models, and then assigns values for new documents.