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Automated vs. Manual Classification: A Multi-Methodological Set of Web Analysis Components

We describe the design and initial development of a tool to automatically classify Web information systems. The applications of such a tool could be found both at the macro-level of economic observation and the micro-level of industry and competitive environment analysis. Components of the tool include mirroring, script-based feature extraction, and five different analysis techniques. The methodological framework comprises statistical clustering, unsupervised neural networks, supervised neural networks, textual analysis, and manual classification. Preliminary results are presented for the prototype implementation and prove encouraging. Even at this early stage they provide a concrete response to open questions about how to capture and analyse the content of WIS, its structure, and its dynamic change.