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AS A MATTER OF COURSE, QUANTITATIVE DATA SUCH AS TIME SERIES AND QUARTERLY FIGURES ARE FREQUENTLY USED IN FINANCIAL ANALYSIS. SUCH DATA CAN BE PROCESSED AUTOMATICALLY AND INTERPRETED RATHER EFFICIENTLY. HOWEVER, A SIGNIFICANT PERCENTAGE OF RELEVANT INFORMATION ORIGINATES FROM UNSTRUCTURED SOURCES, PRIMARILY TEXTUAL DATA, WHICH REQUIRE MANUAL (HUMAN) INTERPRETATION. WE EXPLORE EMPIRICALLY HOW MACHINE LEARNING TECHNIQUES CAN PROVIDE SUPPORT FOR ANALYZING AND INTERPRETING SUCH TEXTUAL DATA SOURCES.