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Topic Evolution Based on LDA and Topic Association
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CHU KeMing，LI Fang
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(School of Electronic, Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China)
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Abstract Topic evolution will help people to learn information quickly. In this paper, a method was proposed to discover topic’s evolution over time by topic detection and relating topics in different time periods. The method applies LDA model on temporal documents to extract topics. The number of topics in different time periods is different. Relating topics in consecutive time periods is based on JensenShannon divergence and features similarity. Experiments show that the method can detect new topics and describe topic’s evolution over time effectively. It not only shows that the topics evolve with time, but also that the content of topics change with time.
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Received: 11 May 2010
Published: 30 November 2010
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