Discovering unexpected information from your competitors' web sites
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Maximizing Text-Mining Performance
IEEE Intelligent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
International Journal of Open Source Software and Processes
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A method is described for real-time market intelligence and competitive analysis. News stories are collected online for a designated group of companies. The goal is to detect critical differences in the text written about a company versus the text for its competitors. A solution is found by mapping the task into a non-stationary text categorization model. The overall design consists of the following components: (a) a real-time crawler that monitors newswires for stories about the competitors (b) a conditional document retriever that selects only those documents that meet the indicated conditions (c) text analysis techniques that convert the documents to a numerical format (d) rule induction methods for finding patterns in data (e) presentation techniques for displaying results. The method is extended to combine text with numerical measures, such as those based on stock prices and market capitalizations, that allow for more objective evaluations and projections.