Discovering News Topics from Microblogs Based on Hidden Topics Analysis and Text Clustering
Discovering News Topics from Microblogs Based on Hidden Topics Analysis and Text Clustering
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发表时间：
2012
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通讯作者：
Liu Ming
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作者：
Liu Ming
提出了一种从大规模微博短帖中提取新闻主题的方法。通过隐藏主题分析，较好地解决了短文本的相似性度量问题。在每个时间窗口中，根据新闻的特点，选择最有可能谈论新闻事件的短帖子。然后，使用两级K-means-hierarchical混合聚类方法将所有选定的数据聚类到不同的新闻主题。实验结果表明，该方法在大规模微博数据集上具有良好的效果。
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.