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With the emergence of the World Wide Web, analyzing and improving Web communication has become essential to adapt the Web content to the visitors' expectations. Web communication analysis is traditionally performed by Web analytics software, which produce long lists of page-based audience metrics. These results suffer from page synonymy, page polysemy, page temporality, and page volatility. In addition, the metrics contain little semantics and are too detailed to be exploited by organization managers and chief editors, who need summarized and conceptual information to take high-level decisions. To obtain such metrics, we mine the content of the Web pages output by the Web server. For a given taxonomy covering the Web site knwoledge domain, we compute the term weights in the output pages and we aggregate them using OLAP tools, in order to obtain concept-based metrics representing the audience of the Web site topics. To demonstrate how our approach solves the cited problems, we actually compute concept-based metrics with SQL Server OLAP Analysis Service and our prototype WASA for a number of case studies. Finally, we validate our results against a popular Web analytics tool.