IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/119875.html
   My bibliography  Save this paper

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

Author

Listed:
  • Limosani, Michele
  • Millemaci, Emanuele
  • Mustica, Paolo

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.

Suggested Citation

  • Limosani, Michele & Millemaci, Emanuele & Mustica, Paolo, 2023. "An efficient Bayes classifier for word classification: an application on the EU Recovery and Resilience Plans," MPRA Paper 119875, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:119875
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/119875/1/MPRA_paper_119875.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Phoebe Koundouri & Stathis Devves & Angelos Plataniotis, 2021. "Alignment of the European Green Deal, the Sustainable Development Goals and the European Semester Process: Method and Application," DEOS Working Papers 2113, Athens University of Economics and Business.
    2. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    4. Franca Debole & Fabrizio Sebastiani, 2005. "An analysis of the relative hardness of Reuters‐21578 subsets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(6), pages 584-596, April.
    5. Mundaca, Luis & Markandya, Anil, 2016. "Assessing regional progress towards a ‘Green Energy Economy’," Applied Energy, Elsevier, vol. 179(C), pages 1372-1394.
    6. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    2. Augustine Denteh & Helge Liebert, 2022. "Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment," Papers 2201.07072, arXiv.org, revised Apr 2023.
    3. Verhagen, Mark D., 2023. "Using machine learning to monitor the equity of large-scale policy interventions: The Dutch decentralisation of the Social Domain," SocArXiv qzm7y, Center for Open Science.
    4. Ay, Jean-Sauveur & Le Gallo, Julie, 2021. "The Signaling Values of Nested Wine Names," Working Papers 321851, American Association of Wine Economists.
    5. Chen, Ruoyu & Jiang, Hanchen & Quintero, Luis E., 2023. "Measuring the value of rent stabilization and understanding its implications for racial inequality: Evidence from New York City," Regional Science and Urban Economics, Elsevier, vol. 103(C).
    6. Ballestar, María Teresa & Mir, Miguel Cuerdo & Pedrera, Luis Miguel Doncel & Sainz, Jorge, 2024. "Effectiveness of tutoring at school: A machine learning evaluation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    7. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    8. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    10. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    11. Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    12. Blankenship, Brian & Aklin, Michaël & Urpelainen, Johannes & Nandan, Vagisha, 2022. "Jobs for a just transition: Evidence on coal job preferences from India," Energy Policy, Elsevier, vol. 165(C).
    13. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    14. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    15. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    16. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    17. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.
    18. Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2020. "Neural Network Models for Empirical Finance," JRFM, MDPI, vol. 13(11), pages 1-22, October.
    19. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    20. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.

    More about this item

    Keywords

    textual analysis; Prior Adaptive Bayes classifier; Recovery and Resilience Plans; Sustainable Development Goals; pro-environment policy;
    All these keywords.

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • H22 - Public Economics - - Taxation, Subsidies, and Revenue - - - Incidence
    • O44 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Environment and Growth

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:119875. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.