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首页> 外文期刊>Procedia Computer Science >Towards Efficient Online Topic Detection through Automated Bursty Feature Detection from Arabic Twitter Streams
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Towards Efficient Online Topic Detection through Automated Bursty Feature Detection from Arabic Twitter Streams

机译：通过阿拉伯语Twitter流中的自动突发特征检测实现高效的在线主题检测

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摘要

Detecting trending topics or events from Twitter is an active research area. The first step in detecting such topics focuses on efficiently capturing textual features that exhibit an unusual high rate of appearance during a specific timeframe. Previous work in this area has resulted in coining the term “detecting bursty features” to refer to this step. In this paper, TFIDF, entropy, and stream chunking are adapted to investigate a new technique for detecting bursty features from an Arabic Twitter stream. Experimental results comparing bursty features extracted from Twitter streams, to Twitter’s trending Hashtags and headlines from local news agencies during the same time frame from which tweets were collected, show a great deal of overlap indicating that the presented algorithm is capable of detecting meaningful bursty features.
机译：从Twitter检测趋势主题或事件是一个活跃的研究领域。检测此类主题的第一步着重于有效捕获在特定时间范围内表现出异常高出现率的文本特征。在该领域的先前工作导致创造了术语“检测突发特征”来指代此步骤。在本文中，TFIDF，熵和流分块适用于研究一种从阿拉伯语Twitter流中检测突发特征的新技术。实验结果比较了从Twitter流中提取的突发特征，以及在收集推文的同一时间段内Twitter的趋势标签和当地新闻社的头条新闻，这些发现有很多重叠之处，表明所提出的算法能够检测出有意义的突发特征。

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