Abstract
Nowadays, online social networks such as Facebook and Twitter become increasingly popular. These social media channels allow people to create, share, and comment on information about anything related to their real-life. Such information is very useful for various application domains, e.g., decision support systems or online advertising.
In this paper, we propose a comprehensive framework for retrieving relevant contents from online social network data. Our approach is proposed on the basic of the Vector Space Model and Support Vector Machine to process and classify raw text data. Our experiments demonstrate the utility and accuracy of the framework in retrieving fashion related contents from Twitter and Facebook.
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Dang, N.C., De la Prieta, F., Corchado, J.M., Moreno, M.N. (2016). Framework for Retrieving Relevant Contents Related to Fashion from Online Social Network Data. In: de la Prieta, F., et al. Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. PAAMS 2016. Advances in Intelligent Systems and Computing, vol 473. Springer, Cham. https://doi.org/10.1007/978-3-319-40159-1_28
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DOI: https://doi.org/10.1007/978-3-319-40159-1_28
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