Web newspapers provide a valuable resource for information. In order to benefit more from the available information, text mining techniques can be applied. However, because each newspaper page often covers a lot of unrelated topics, page-based data mining will not always give useful results. In order to improve on complete-page mining, we present an approach based on extracting the individual news items from the web pages and mining these separately. Automatic news item extraction is a difficult problem, and in this paper we also provide strategies solving that task. We study the quality of the news item extraction, and also provide results from clustering the extracted news items.