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This paper describes the preliminary results of a system for extracting sentiments opinioned with regard with named entities. It also combines rule-based classification, statistics and machine learning in a new method. The accuracy and speed of extraction and classification are crucial. The service oriented architecture permits the end-user to work with a flexible interface in order to produce applications that range from aggregating consumer feedback on commercial products to measuring public opinion on political issues from blog and forums. The experiment has two versions available for testing, one with concrete extraction results and sentiment calculus and the other with internal metrics validation results.