Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Detecting topic evolution in scientific literature: how can citations help?
Proceedings of the 18th ACM conference on Information and knowledge management
Trend analysis model: trend consists of temporal words, topics, and timestamps
Proceedings of the fourth ACM international conference on Web search and data mining
Predicting future reviews: sentiment analysis models for collaborative filtering
Proceedings of the fourth ACM international conference on Web search and data mining
A word at a time: computing word relatedness using temporal semantic analysis
Proceedings of the 20th international conference on World wide web
Tracking trends: incorporating term volume into temporal topic models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.