Events Insights Extraction from Twitter Using LDA and Day-Hashtag Pooling
Events Insights Extraction from Twitter Using LDA and Day-Hashtag Pooling
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DOI：
10.1145/3366030.3366090
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Muhammad Haseeb U. R. Rehman Khan;Kei Wakabayashi;Satoshi Fukuyama
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作者：
Muhammad Haseeb U. R. Rehman Khan;Kei Wakabayashi;Satoshi Fukuyama
从Twitter数据中提取新闻是一个热门话题。但我们能提取出比新闻更多的信息吗？本研究的目的是发现，无论是新闻是唯一的信息，可以从Twitter的数据，或它包含了更多的见解，对真实的生活事件。因此，我们引入了一种分析Twitter原始内容的技术。在对推文数据进行预处理后，我们应用主题标签池，并使用现有的主题建模算法Latent Dirichlet Allocation（LDA）提取主题，而无需修改其核心机制。在第二部分中，每天推文的估计数量和每个主题的相关热门标签使用日标签池计算。最后，构造连续时间序列图进行主题分析。我们的研究结果显示了突发新闻检测，话题流行度，人们感知事件的方式，现实生活中的事件随着时间的推移和之前和之后的影响，一个特定的事件的有趣的结果。
News extraction from Twitter data is a hot topic. But can we extract much more than just news? The purpose of this research is to find, either news is the only information which can be extracted from Twitter data or it contains much more insights about real life events. So, we introduce a technique for analysis of Twitter's raw content. After pre-processing of tweets data, we apply hashtag pooling and extract topics using available topic modeling algorithm Latent Dirichlet Allocation (LDA) without modifying its core machinery. In the second part, estimated number of tweets per day and correlated top hashtags for each topic are calculated using day-hashtag pooling. Finally, the continues time series graph is constructed for topic analysis. Our findings show interesting results of bursty news detection, topic popularity, people's way to perceiving an event, real-life event's transition over time and before & after affects of a specific event.