基于多特征的微博突发事件检测算法

为了降低社交媒体中突发事件带来的危害，提出一种基于多特征的微博突发事件检测算法。该算法融合了文本情感过滤和用户影响力计算方法。首先，通过噪声过滤和情感过滤得到饱含负面情感的微博文本；然后，采用提出的用户影响力计算方法并结合突发词提取算法来提取突发词特征；最后，引入凝聚式层次聚类算法对突发词集进行聚类，从中提取突发事件。通过实验检测，准确率为66.84%，验证了该方法能有效地对突发事件进行检测。

Microblog bursty events detection algorithm based on multi-feature

In order to reduce the harm caused by bursty events in social media, a multi-feature based microblog bursty events detection algorithm was proposed. The algorithm combines text emotion filtering and user influence calculation methods. Firstly, the microblog text with negative emotion was obtained through noise filtering and emotion filtering. Then the proposed user influence calculation method was combined with the burst word extraction algorithm to extract the characteristics of burst words. Finally, a cohesive hierarchical clustering algorithm was introduced to cluster bursty word sets, and extract bursty events from them. In the experimental test, the accuracy is 66.84%, which proves that the proposed method can effectively detect bursty events.