Abstract
The purpose of this paper is to investigate the application of text classification in Hypatia, the digital library of Technological Educational Institute of Athens, in order to provide an automated classification tool as an alternative to manual assignments. The crucial point in text classification is the selection of the most important term-words for document representation. Classic weighting method TF.IDF was investigated. Our document collection consists of 718 abstracts in Medicine, Tourism and Food Technology. Classification was conducted utilizing 14 classifiers available on WEKA. Classification process yielded an excellent ~97 % precision score.
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Vorgia, F., Triantafyllou, I., Koulouris, A. (2017). Hypatia Digital Library: A Text Classification Approach Based on Abstracts. In: Kavoura, A., Sakas, D., Tomaras, P. (eds) Strategic Innovative Marketing. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-33865-1_89
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DOI: https://doi.org/10.1007/978-3-319-33865-1_89
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