Time stamped texts or text sequences are ubiquitous in real life, such as news reports. Tracking the topic evolution of these texts has been an issue of considerable interest. Recent work has developed methods of tracking topic shifting over long time scales. However, most of these researches focus on a large corpus. Also, they only focus on the text itself and no attempt have been made to explore the temporal distribution of the corpus, which could provide meaningful and comprehensive clues for topic tracking. In this paper, we formally address this problem and put forward a novel method based on the topic model. We investigate the temporal distribution of news reports of a specific event and try to integrate this information with a topic model to enhance the performance of topic model. By focusing on a specific news event, we try to reveal more details about the event, such as, how many stages are there in the event, what aspect does each stage focus on, etc.
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- News Topic Evolution Tracking by Incorporating Temporal Information
- Springer Berlin Heidelberg