AUTHORS: Amjad Jumaah Frhan

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ABSTRACT: 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.

KEYWORDS: Event detection, visualization, Named Entity recognition, Topical PageRank, Hierarchical Agglomerative clustering, term frequency–inverse document frequency.

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