Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/3447
Title: Categorization of web sites in Turkey with SVM
Authors: Püskülcü, Halis
Şimşek, Kadir
Issue Date: 2004
Publisher: Izmir Institute of Technology
Abstract: In this study of topic .Categorization of Web Sites in Turkey with SVM. after a brief introduction to what the World Wide Web is and a more detailed description of text categorization and web site categorization concepts, categorization of web sites including all prerequisites for classification task takes part. As an information resource the web has an undeniable importance in human life. However the huge structure of the web and its uncontrolled growth led to new information retrieval research areas to be risen in last years. Web mining, the general name of these studies, investigates activities and structures on the web to automatically discover and gather meaningful information from the web documents. It consists of three subfields: .Web Structure Mining., .Web Content Mining. and .Web Usage Mining.. In this project, web content mining concept was applied on the web sites in Turkey during the categorization process. Support Vector Machine, a supervised learning method based on statistics and principle of structural risk minimization is used as the machine learning technique for web site categorization. This thesis is intended to draw a conclusion about web site distributions with respect to thematic categorization based on text. The popular web directory Yahoo.s 12 top level categories were used in this project. Beside of the main purpose, we gathered several statistical descriptive informations about web sites and contents used in html pages. Metatag usage percentages, html design structures and plug-in usage are some of these information. The processes taken through solution, start with employing a web downloader which downloads web page contents and other information such as frame content from each web site. Next, manipulating, parsing and simplifying the downloaded documents takes place. At this point, preperations for categorization task are completed. Then, by applying Support Vector Machine (SVM) package SVMLight developed by Thorsten Joachims, web sites are classified under given categories. The classification results obtained in the last section show that there are some over-lapping categories exist and accuracy and precision values are between 60-80. In addition to categorization results, we saw that almost 17 of web sites utilize html frames and 9367 web sites include metakeywords.
Description: Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2004
Includes bibliographical references (leaves: 61-63)
Text in English; Abstract: Turkish and English
ix, 70 leaves
URI: http://hdl.handle.net/11147/3447
Appears in Collections:Master Degree / Yüksek Lisans Tezleri

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