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Natural Language Processing as Feature Extraction Method for Building Better Predictive Models

Natural Language Processing as Feature Extraction Method for Building Better Predictive Models

Goran Klepac, Marko Velić
Copyright: © 2017 |Pages: 25
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch078
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MLA

Klepac, Goran, and Marko Velić. "Natural Language Processing as Feature Extraction Method for Building Better Predictive Models." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1913-1937. https://doi.org/10.4018/978-1-5225-1759-7.ch078

APA

Klepac, G. & Velić, M. (2017). Natural Language Processing as Feature Extraction Method for Building Better Predictive Models. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 1913-1937). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch078

Chicago

Klepac, Goran, and Marko Velić. "Natural Language Processing as Feature Extraction Method for Building Better Predictive Models." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1913-1937. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch078

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Abstract

This chapter covers natural language processing techniques and their application in predicitve models development. Two case studies are presented. First case describes a project where textual descriptions of various situations in call center of one telecommunication company were processed in order to predict churn. Second case describes sentiment analysis of business news and describes practical and testing issues in text mining projects. Both case studies depict different approaches and are implemented in different tools. Language of the texts processed in these projects is Croatian which belongs to the Slavic group of languages with more complex morphologies and grammar rules than English. Chapter concludes with several points on the future research possible in this domain.

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