Web Content Classification Using Artificial Neural Networks

Authors

  • Esra Nergis Güven Gazi Üniversitesi, Bilgisayar Mühendisliği Bölümü
  • Hakan Onur Gazi Üniversitesi, Bilgisayar Mühendisliği Bölümü
  • Şeref Sağıroğlu Gazi Üniversitesi, Bilgisayar Mühendisliği Bölümü

DOI:

https://doi.org/10.15612/BD.2008.332

Keywords:

Artificial neural networks, Text categorization, Content classification, Web page categorization, Information management

Abstract

Recent developments and widespread usage of the Internet have made business and processes to be completed faster and easily in electronic media. The increasing size of the stored, transferred and processed data brings many problems that affect access to information on the Web. Because of users’ need get to access to the information in electronic environment quickly, correctly and appropriately, different methods of classification and categorization of data are strictly needed. Millions of search engines should be supported with new approaches every day in order for users to get access to relevant information quickly. In this study, Multilayered Perceptrons (MLP) artificial neural network model is used to classify the web sites according to the specified subjects. A software is developed to select the feature vector, to train the neural network and finally to categorize the web sites correctly. It is considered that this intelligent approach will provide more accurate and secure platform to the Internet users for classifying web contents precisely.

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Published

2008-04-30

How to Cite

Güven, E. N., Onur, H., & Sağıroğlu, Şeref. (2008). Web Content Classification Using Artificial Neural Networks. Information World, 9(1), 158-178. https://doi.org/10.15612/BD.2008.332

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