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Large text corpora with news, customer mail and reports, or Web 2.0 contributions offer a great potential for enhancing business-intelligence applications. The authors propose a framework for performing text analytics on such data in a versatile, efficient, and scalable manner. While much of the prior literature has emphasized mining keywords or tags in blogs or social-tagging communities, they emphasize the analysis of interesting phrases. These include named entities, important quotations, market slogans, and other multi-word phrases that are prominent in a dynamically derived ad-hoc subset of the corpus, e.g., being frequent in the subset but relatively infrequent in the overall corpus.
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