On the Automated Classification of Web Sites

TL;DR

This paper explores automated web site classification, emphasizing HTML metatags and proposing a targeted spidering approach with metadata extraction to improve classification accuracy.

Contribution

It introduces a system for classifying web sites into industry categories using metadata and text features, and discusses a framework for automated metadata creation.

Findings

HTML metatags are effective for classification but underused.

Targeted spidering improves metadata collection for classification.

System achieves promising accuracy with combined features.

Abstract

In this paper we discuss several issues related to automated text classification of web sites. We analyze the nature of web content and metadata in relation to requirements for text features. We find that HTML metatags are a good source of text features, but are not in wide use despite their role in search engine rankings. We present an approach for targeted spidering including metadata extraction and opportunistic crawling of specific semantic hyperlinks. We describe a system for automatically classifying web sites into industry categories and present performance results based on different combinations of text features and training data. This system can serve as the basis for a generalized framework for automated metadata creation.