  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.
