Twitter is an interesting platform for the dissemination of news. The real-time nature and brevity of the tweets are conductive to sharing of information related to important events as they unfold.Numerous consumer reviews of topics are now available on the Internet. Automatically identifies the important aspects of topics from online consumer reviews. Our method provides an efficient way to accurately categorize comic topic recommendation without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly identify viral memes that might enrich marketing decisions, among others. We filter the stream of incoming tweets to remove junk tweets using a text classification algorithm.We also compare the performance of different supervised SVM text classification algorithms for this task. This study concentrates on analyzing potential and dynamic user correlations, based on topic-aware similarity and behavioral influence, which may help us to discover communities in social networking sites.
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