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comprehensive and quality feeds for real-time event detection. In this paper, we present a novel adaptive keyword identification approach to retrieve a greater amount of event relevant content. This approach continuously monitors emerging hashtags and rates them by their similarity to specific pre-defined event hashtags using TF
learns topics and keywords from a domain data stream. The conceptualization enriches a UIP, consisting of user interests modeled as terms and term-weight, by providing contextual information of the UIP. For this, the topic hierarchy extracts topic-deterministic keywords and their semantic associations with domain topics
. Luckily, tweets always show up with rich user-generated hash tags as keywords. In this paper, we propose a novel topic model to handle such semi-structured tweets, denoted as Hash tag Graph based Topic Model (HGTM). By utilizing relation information between hash tags in our hash tag graph, HGTMestablishes word semantic
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