Abstract
Big data analytics provides the large volumes of data. The data are collected from a various sources including social networks, videos, digital images, sensors, and sales transaction records. Hot news is the information about current news of anywhere in the world. The information is receiving about an event that just happened. Breaking news reported while an event is on-going. In existing system,used CNN based hash tag recommendation along with the increasing requirements. Convolutionalneural networks (CNNs) for many natural language processing tasks, to perform the hashtag recommendation problem. In this work, proposedan efficient rule-based technique for merging news and social streams in real-time, Rule-based classifier makes use of a set of IF-THEN rules for classification. Twitter hash tags function as a key connection between Twitter crowds and the news media. To improve the efficiency and coverage of a state-of-the-art hash tag recommendation model by proposing new techniques for data collection and feature computation
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