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Identification of Perceived Challenges in the Green Energy Transition by Turkish Society through Sentiment Analysis

Author

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  • Ugur Bilgin

    (Department of Industrial Engineering, Graduate School of Natural and Applied Sciences, Yildiz Technical University, 34349 Istanbul, Turkey)

  • Selin Soner Kara

    (Department of Industrial Engineering, Graduate School of Natural and Applied Sciences, Yildiz Technical University, 34349 Istanbul, Turkey)

Abstract

Green energy refers to energy derived from renewable sources such as solar, wind, hydro, and biomass, which are environmentally sustainable. It aims to reduce reliance on fossil fuels and mitigate environmental impacts. In the Turkish context, alongside positive sentiments regarding the establishment of energy plants, there are also prevalent negative perspectives. Societal responses to the transition towards green energy can be effectively gauged through the analysis of individual comments. However, manually examining thousands of comments is both time-consuming and impractical. To address this challenge, this study proposes the integration of the Transformer method, a Natural Language Processing (NLP) technique. This study presents a defined NLP procedure that utilizes a multi-labeled NLP model, with a particular emphasis on the analysis of comments on social media classified as “dirty text”. The primary objective of this investigation is to ascertain the evolving perception of Turkish society regarding the transition to green energy over time and to conduct a comprehensive analysis utilizing NLP. The study utilizes a dataset that is multi-labeled, wherein emotions are not equally represented and each dataset may contain multiple emotions. Consequently, the measured accuracy rates for the risk, environment, and cost labels are, respectively, 0.950, 0.924, and 0.913, whereas the ROC AUC scores are 0.896, 0.902, and 0.923. The obtained results indicate that the developed model yielded successful outcomes. This study aims to develop a forecasting model tailored to green energy to analyze the current situation and monitor societal behavior dynamically. The central focus is on determining the reactions of Turkish society during the transition to green energy. The insights derived from the study aim to guide decision-makers in formulating policies for the transition. The research concludes with policy recommendations based on the model outputs, providing valuable insights for decision-makers in the context of the green energy transition.

Suggested Citation

  • Ugur Bilgin & Selin Soner Kara, 2024. "Identification of Perceived Challenges in the Green Energy Transition by Turkish Society through Sentiment Analysis," Sustainability, MDPI, vol. 16(8), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3367-:d:1377456
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    References listed on IDEAS

    as
    1. Marco Segreto & Lucas Principe & Alexandra Desormeaux & Marco Torre & Laura Tomassetti & Patrizio Tratzi & Valerio Paolini & Francesco Petracchini, 2020. "Trends in Social Acceptance of Renewable Energy Across Europe—A Literature Review," IJERPH, MDPI, vol. 17(24), pages 1-19, December.
    2. Serena Y. Kim & Koushik Ganesan & Princess Dickens & Soumya Panda, 2021. "Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    3. Dong, Xuefan & Lian, Ying, 2021. "A review of social media-based public opinion analyses: Challenges and recommendations," Technology in Society, Elsevier, vol. 67(C).
    Full references (including those not matched with items on IDEAS)

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