An efficient Bayes classifier for word classification: an application on the EU Recovery and Resilience Plans

Michele Limosani, Emanuele Millemaci and Paolo Mustica

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes the Prior Adaptive Bayes (PAB) classifier, a new algorithm to assign words appearing in a text to their respective topics. It is an adaption of the Bayes classifier where, as the prior probabilities of classes, their posterior probabilities associated with the adjacent words are used. Simulations show an improvement of more than 20% over the standard Bayes classifier. The PAB classifier is applied to the Recovery and Resilience Plans (RRPs) of the 27 European Union member states to evaluate their alignment with the environmental dimension of the Sustainable Development Goals (SDGs) as compared to the socioeconomic one. Results show that the attention paid by the countries to the pro-environment SDGs increases with the funds per capita assigned, the gap in the environmental endowment and the touristic attractiveness. Finally, the environmental dimension appears associated positively with available GDP growth projections for the next few years.

Keywords: textual analysis; Prior Adaptive Bayes classifier; Recovery and Resilience Plans; Sustainable Development Goals; pro-environment policy (search for similar items in EconPapers)
JEL-codes: C82 H22 O44 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-eec and nep-env
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