A Comprehensive Study of Features and Algorithms for URL-Based Topic Classification

• Published in: ACM transactions on the web Vol. 5; no. 3; pp. 1 - 29
• Main Authors: Baykan, Eda, Henzinger, Monika, Marian, Ludmila, Weber, Ingmar
• Format: Journal Article
• Language: English
• Published: 01.07.2011
• Subjects: Algorithms; Classification; Classifiers; Recall; Websites; World Wide Web
• ISSN: 1559-1131; 1559-114X
• DOI: 10.1145/1993053.1993057

Given only the URL of a Web page, can we identify its topic? We study this problem in detail by exploring a large number of different feature sets and algorithms on several datasets. We also show that the inherent overlap between topics and the sparsity of the information in URLs makes this a very challenging problem. Web page classification without a page’s content is desirable when the content is not available at all, when a classification is needed before obtaining the content, or when classification speed is of utmost importance. For our experiments we used five different corpora comprising a total of about 3 million (URL, classification) pairs. We evaluated several techniques for feature generation and classification algorithms. The individual binary classifiers were then combined via boosting into metabinary classifiers. We achieve typical F-measure values between 80 and 85, and a typical precision of around 86. The precision can be pushed further over 90 while maintaining a typical level of recall between 30 and 40.