EXPERIMENTED KINETIC ENERGY AS FEATURES FOR NATURAL LANGUAGE CLASSIFICATION

Daia Alexandru

No drwc6, OSF Preprints from Center for Open Science

Abstract: This article describes various uses of kinetic Energy in Natural Language Processing (NLP) and why Natural Language Processing could be used in trading, with the potential to be use also in other applications, including psychology and medicine. Kinetic energy discovered by great Romanian mathematician Octave Onicescu (1892-1983), allows to do feature engineering in various domains including NLP which we did in this experiment. More than that we have run a machine learning model called xgboost to see feature importance and the features extracted by xgboost where captured the most important, in order to classify for simplicity of reader some authors by their content and type of writing

Date: 2019-06-20
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:drwc6

DOI: 10.31219/osf.io/drwc6

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