Abstract A method of news topics extraction from large-scale short posts of microblogging-service is proposed. Through the hidden topic analysis, the similarity measurement of short texts is solved well. In every time window, the short posts which are most likely to talk about news events are selected according to the characteristics of the news. Then, a two-level K-means-hierarchical hybrid clustering method is used to cluster all the selected data into different news topics. The experimental results show the proposed method works well on large-scale microblog dataset.
LU Rong,XIANG Liang,LIU Ming-Rong等. Discovering News Topics from Microblogs Based on Hidden Topics Analysis and Text Clustering[J]. , 2012, 25(3): 382-387.
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