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

Limosani, Michele and Millemaci, Emanuele and Mustica, Paolo (2023): An efficient Bayes classifier for word classification: an application on the EU Recovery and Resilience Plans.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: textual analysis; Prior Adaptive Bayes classifier; Recovery and Resilience Plans; Sustainable Development Goals; pro-environment policy
• Subjects: C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C82 - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data ; Data Access; H - Public Economics > H2 - Taxation, Subsidies, and Revenue > H22 - Incidence; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O44 - Environment and Growth

References

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