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A RoBERTa Approach for Automated Processing of Sustainability Reports

Author

Listed:
  • Merih Angin

    (Department of International Relations, Koc University, Istanbul 34450, Turkey)

  • Beyza Taşdemir

    (Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
    These authors contributed equally to this work.)

  • Cenk Arda Yılmaz

    (Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
    These authors contributed equally to this work.)

  • Gökcan Demiralp

    (Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
    These authors contributed equally to this work.)

  • Mert Atay

    (Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey)

  • Pelin Angin

    (Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey)

  • Gökhan Dikmener

    (United Nations Development Programme, SDG AI Lab, Istanbul 34381, Turkey)

Abstract

There is a strong need and demand from the United Nations, public institutions, and the private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by community volunteers. Results demonstrate that especially fine-tuned RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

Suggested Citation

  • Merih Angin & Beyza Taşdemir & Cenk Arda Yılmaz & Gökcan Demiralp & Mert Atay & Pelin Angin & Gökhan Dikmener, 2022. "A RoBERTa Approach for Automated Processing of Sustainability Reports," Sustainability, MDPI, vol. 14(23), pages 1-25, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16139-:d:992144
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    References listed on IDEAS

    as
    1. Namsuk Kim & Marcelo LaFleur, 2020. "What does the United Nations “say” about global agenda? An exploration of trends using natural language processing for machine learning," Working Papers 171, United Nations, Department of Economics and Social Affairs.
    2. Málovics, György & Csigéné, Noémi Nagypál & Kraus, Sascha, 2008. "The role of corporate social responsibility in strong sustainability," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 37(3), pages 907-918, June.
    3. Luis Miguel Fonseca & José Pedro Domingues & Alina Mihaela Dima, 2020. "Mapping the Sustainable Development Goals Relationships," Sustainability, MDPI, vol. 12(8), pages 1-15, April.
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