Abstract
In the paper we build classifiers of texts reflecting opinions of currency market analysts about euro/dollar rate. The classifiers use various combinations of classes: growth, fall, constancy, not-growth, not-fall. The process includes term selection based on criterion of word specificity and model selection using technique of inductive modeling. We shortly describe our tools for these procedures. In the experiments we evaluate quality of classifiers and their sensibility to term list. The results proved to be positive and therefore the proposed approach can be a useful addition to the existing quantitative methods. The work has a practical orientation.
Work done under partial support of the British Petroleum grant (RPANEPA-S1/2013).
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Koshulko, O., Alexandrov, M., Danilova, V. (2014). Forecasting Euro/Dollar Rate with Forex News. In: Métais, E., Roche, M., Teisseire, M. (eds) Natural Language Processing and Information Systems. NLDB 2014. Lecture Notes in Computer Science, vol 8455. Springer, Cham. https://doi.org/10.1007/978-3-319-07983-7_19
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DOI: https://doi.org/10.1007/978-3-319-07983-7_19
Publisher Name: Springer, Cham
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