AUTHORS: Amjad Jumaah Frhan

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ABSTRACT: Event detection and tracking is always been an efficient strategy of automation. Detecting significant real-world events from the given database or documents using past knowledge has garnered immense research interest in the recent years. Researches have garnered huge in numbers which focuses on utilizing the data like updates, status messages, shared pictures, etc. in social media to identify the occurrence of events. The most popular events of environmental, political, cultural or everyday importance are detected and tracked for various applications all over the world. However detecting the number of common events from the social media content requires efficient strategies as the size of the content and number of users is large, leading to large data to be processed. In order to avoid the limitations of the existing event detection schemes, this paper presents a new approach named Event WebClickviz. This model visualizes the user data and then analyses the similarity between the data to detect the events. Initially the event detection process is considered as a clustering problem as best results are obtained for clustering algorithms. 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, online social networks, WebClickviz, clickstream

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