Framework for Retrieving Relevant Contents Related to Fashion from Online Social Network Data View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Nhan Cach Dang , Fernando De la Prieta , Juan Manuel Corchado , María N. Moreno

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. More... »

PAGES

335-347

References to SciGraph publications

  • 2012. A Survey of Text Classification Algorithms in MINING TEXT DATA
  • Book

    TITLE

    Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection

    ISBN

    978-3-319-40158-4
    978-3-319-40159-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-40159-1_28

    DOI

    http://dx.doi.org/10.1007/978-3-319-40159-1_28

    DIMENSIONS

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