Trend Extraction Method Using Co-occurrence Patterns from Tweets
Trend Extraction Method Using Co-occurrence Patterns from Tweets
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DOI：
10.1109/iiai-aai.2015.263
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Shotaro Noda;K. Fujita
中科院分区：
文献类型：
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作者：
Shotaro Noda;K. Fujita
我们可以随时发布的信息，如个人事件使用Twitter的流行的微型博客服务之一。然而，信息的收集仅限于人力，因此，自动收集趋势的方法是重要的。现有的网络服务专注于获取趋势的推文数量。然而，在提取趋势时出现了时滞。在本文中，我们提出的趋势提取方法的twitter在真实的时间关注的同现模式。我们的系统可以同时学习新的关键模式，而不仅仅是使用以前拾取的趋势双项。此外，我们评估的效率所提出的方法提取的趋势，从twitter的比较实验。我们证明，我们提出的方法可以准确地提取和广泛的时间滞后相比，现有的服务（真实的时间雅虎搜索）。
We can feel free to post the information such as personal events using Twitter one of the popular micro-blogging service. However, the collection of information is limited by the human power only, therefore, the method of collecting trends automatically is important. Existing web services focus on the number of tweets for getting trends. However, a time lag was occurred for extracting the trends. In this paper, we propose the trend extraction method for twitter in real time by paying attention to the co-occurrence patterns. Our system can learn the new key patterns at the same time not only using the picked up trend biterms, previously. Furthermore, we evaluate the efficiency of the proposed method of extracting the trends from twitter by the comparative experiments. We demonstrate that our proposed method can extract accurately and widely without time-lags compared with the existing service (Real time Yahoo Search).