Detecting hot topics in technology news streams

Bo You, Ming Liu, Bingquan Liu, Xiaolong Wang
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引用次数: 1

Abstract

Detecting hot topics with a fine granularity in technology news streams is an interesting and important problem given the large amount of reports and a relatively narrow range of topics. In this paper, a three-phase method is proposed. In the first phase, the document topic distribution vector is generated and keywords are extracted for each document using topic model pachinko allocation. In the second phase, the documents are clustered based on the document topic distribution vector obtained from the previous phase using affinity propagation. And in the last phase, actual events denoted by combinations of keywords within each cluster are found out using frequent pattern mining algorithms. We evaluate our approach on a collection of technology news reports from various sites in a fixed time period. T he results show that this method is effective.
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检测技术新闻流中的热点话题
在科技新闻流中,对热点话题进行细粒度检测是一个有趣而重要的问题。本文提出了一种三相方法。在第一阶段,使用主题模型弹珠分配生成文档主题分布向量,并为每个文档提取关键字。在第二阶段,使用亲和传播基于从前一阶段获得的文档主题分布向量对文档进行聚类。在最后阶段,使用频繁模式挖掘算法找出每个聚类中由关键字组合表示的实际事件。我们根据固定时间段内来自不同网站的技术新闻报道来评估我们的方法。结果表明,该方法是有效的。
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