【摘 要】
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Topic detection and tracking are attractive research focus in social networks.Clustering documents and recognizing topic from clusters is the main concern of topic detection.Topic tracking try to foll
【机 构】
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Library, China University of Political Science and Law
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Topic detection and tracking are attractive research focus in social networks.Clustering documents and recognizing topic from clusters is the main concern of topic detection.Topic tracking try to follow topics evolution over time.However, find correlation between topics is still an open issue.This article proposed a progressive clustering strategy which uses a three stage strategy, in first stage, text clustering algorithm is used for macro topic detection, in the second stage, term clustering is used for medium topic detection and finally third stage find relationships between micro topics by temporal text mining.The presented method was implemented for processing massive data obtained from multi social networks, experimental result shows it can reveal latent correlations between topics.
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