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

  • Nhan Cach DangEmail author
  • Fernando De la Prieta
  • Juan Manuel Corchado
  • María N. Moreno
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 473)


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.


Text mining TF-IDF Vector Space Model Support Vector Machine 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ikonomakis, M., Kotsiantis, S., Tampakas, V.: Text classification using machine learning techniques. WSEAS Transactions on Computers 4(8), 966–974 (2005)Google Scholar
  2. 2.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys (CSUR) 34(1), 1–47 (2002)CrossRefGoogle Scholar
  3. 3.
    Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Mining text data, pp. 163–222. Springer (2012)Google Scholar
  4. 4.
    Turney, P.D., Pantel, P.: From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research 37(1), 141–188 (2010)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Rajaraman, A., Ullman, J.D., Ullman, J.D.: Mining of massive datasets, vol. 77. Cambridge University Press, Cambridge (2012)Google Scholar
  6. 6.
    Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)Google Scholar
  7. 7.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques: concepts and techniques. Elsevier (2011)Google Scholar
  9. 9.
    Berry Michael, W.: Automatic Discovery of Similar Words. Survey of Text Mining: Clustering, Classification and Retrieval, vol. 200, pp. 24–43. Springer Verlag (2004)Google Scholar
  10. 10.
    Kroeze, J.H., Matthee, M.C., Bothma, T.J.D.: Differentiating between data-mining and text-mining terminology. South African Journal of Information Management 6(4) (2004)Google Scholar
  11. 11.
    Nalini, K., Sheela, L.J.: Survey on Text Classification (2014)Google Scholar
  12. 12.
    Berson, A., Smith, S.J.: Data warehousing, data mining, and OLAP. McGraw-Hill, Inc. (1997)Google Scholar
  13. 13.
    Grimmer, J., Stewart, B.M.: Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, p. mps028 (2013)Google Scholar
  14. 14.
    McCallum, A.K.: Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering (1996)Google Scholar
  15. 15.
    McCallum, A.K.: MALLET: A Machine Learning for Language Toolkit (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Nhan Cach Dang
    • 1
    Email author
  • Fernando De la Prieta
    • 2
  • Juan Manuel Corchado
    • 2
  • María N. Moreno
    • 2
  1. 1.HoChiMinh City University of Transport (UT-HCMC)Ho Chi Minh CityVietnam
  2. 2.University of SalamancaSalamancaSpain

Personalised recommendations