In this paper we look at automated classification to determine a metadata attribute related to the 'tone' of a consumer-oriented breast cancer webpage as Medical or Supportive. We use a semantic space model called Hyperspace Analog to Language (HAL), based on word co-occurrence, to provide features for webpage classification. Adaptive K-Local Hyperplane (AKLH), an extension of K Nearest Neighbour, is then applied to training and testing data. We observe 92% classification accuracy on test cases. This combination of methods appears promising for identifying non-trivial metadata attributes of consumer health webpages, with potential use embedded in a search engine or as a meta-data coding support tool.