Performance of Using Tag-based Feature Sets in Web Page Classification

Year 2018, Volume: 22 Issue: 2, 583 - 594, 15.08.2018

Selma Ayşe Özel Havva Esin Ünal İlker Ünal

Abstract

As the Web is a large collection of data growing daily, an automatic Web page classification mechanism is needed to effectively reach to useful information. Majority of the Web pages are in the form of HTML documents, therefore the aim of this study is to explore the effect of HTML tags on classification process, and try to determine the most valuable HTML tags for feature extraction of the classification task. To achieve this goal, we employ 13 different datasets, and use 5 popular classifiers that are SVM, naïve bayes (NB), kNN, C4.5, and OneR. The statistical analysis shows that, the features extracted by using solely the anchor, <p> or <title> tags can be used as an alternative to the features extracted from the whole Web page. SVM is the best among the classifiers used in this study. Using the HTML tags for feature extraction improves classification accuracy.

Keywords

Web mining, Classification; HTML tags; Feature extraction

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