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首页> 外文期刊>WSEAS Transactions on Signal Processing >Hierarchical Agglomerative Clustering Algorithm Based Real-Time Event Detection from Online Social Media Network
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Hierarchical Agglomerative Clustering Algorithm Based Real-Time Event Detection from Online Social Media Network

机译：基于分层凝聚聚类算法从在线社交媒体网络的实时事件检测

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摘要

Event detection from online social networks based on the user behaviour has been a research area which has garnered immense attention in the recent years. Many works have been developed for event detection in multiple social media sources like Twitter, Facebook, YouTube, etc. The user updates including short texts, photos and videos can be utilized in detecting the events. However detecting the number of common events from the social media content requires efficient distinguishing as the size of the content and number of users is large, leading to large data. In this paper, a new approach is proposed named as Event WebClickviz that performs the dual functions of visualization and behavioural analysis based on which the events are detected. In this approach, the event detection problem is modelled as clustering problem. Named Entity recognition with Topical PageRank is employed for extracting the key terms in the texts while the temporal sequences of real values are estimated to build the event sequences. The features are extracted by applying the concept of sentiment analysis using term frequency-inverse document frequency (TF-IDF). Based on these features the content is clustered using Hierarchical Agglomerative clustering algorithm. Thus the event is detected with high efficiency and they are visualized better using the proposed model. The simulation results justify the performance of the proposed Event WebClickviz.
机译：根据用户行为的在线社交网络的事件检测一直是近年来在近年来获得了巨大关注的研究区。在多个社交媒体来源中，许多作品是在推特，Facebook，YouTube等中的多个社交媒体来源中开发的。用户更新，包括短文本，照片和视频，可以在检测到事件时。然而，从社交媒体内容中检测常见事件的数量需要高效区分，因为内容的大小和用户数量大，导致大数据。在本文中，提出了一种名为Event WebClickViz的新方法，该方法基于检测到事件的可视化和行为分析的双重功能。在这种方法中，事件检测问题被建模为聚类问题。使用局部PageRank命名实体识别，用于在文本中提取关键术语，而估计实际值的时间序列以构建事件序列。通过使用术语频率逆文档频率（TF-IDF）应用情绪分析的概念来提取该特征。基于这些功能，内容使用分层凝聚聚类算法群集。因此，通过高效率检测事件，并且使用所提出的模型可以更好地可视化。仿真结果证明了所提出的事件WebClickviz的性能证明了典范。

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