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

Abstract

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.

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Authors
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Citation
Category
Journal Paper
Divisions
Theory and Applications of Algorithms
Journal or Publication Title
ACM Transactions on the Web
ISSN
1559-1131
Publisher
ACM
Place of Publication
New York, NY, USA
Page Range
15:1-15:29
Number
3
Volume
5
Date
July 2011
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