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<i title="Springer International Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kax3wwzwmncwhi472pxbzqsjja" style="color: black;">Advances in Intelligent Systems and Computing</a>
Today, social media services and multiplatform applications such as microblogs, forums and social networks gives people the ability to communicate, interact and generate content which establish social and collaborative backgrounds. These services now embodies the leading and biggest repository containing millions of Big social Data that can be useful for many applications such as measure public sentiment, trends monitoring, reputation management and marketing campaigns. But social media data<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-26690-9_30">doi:10.1007/978-3-319-26690-9_30</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n5deq5c5cjaflbfvk7mw6skgte">fatcat:n5deq5c5cjaflbfvk7mw6skgte</a> </span>
more »... essentially unstructured that's what makes it so interesting and so hard to analyze. Making sense of it and understanding what it means will require all new technologies and techniques, including the emerging field of big data. In addition, social media is a key model of the velocity and variety which are main characteristics of Big Data. In this paper, we propose a new approach to retrieve conversation on microblogging sites that combine Big Data environment and social media analytics solutions. The goal of our approach is to present a more informatives result and solve the information overload problem within Big Data environment. The proposed approach has been implemented and evaluated by comparing it with Google and Twitter Search engines and we obtained very promising results.
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