ENHANCING LDA MODELING WITH BERT FOR ANALYZING SOCIO-CULTURAL DYNAMICS IN SOLID WASTE MANAGEMENT

ÖZYİRMİDOKUZ Esra Kahya (), Eduard Stoica () and Kumru Uyar ()
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ÖZYİRMİDOKUZ Esra Kahya: Erciyes University
Kumru Uyar: Erciyes University

Management of Sustainable Development, 2024, vol. 16, issue 2, 59-71

Abstract: The rapid depletion of global resources and the increasing challenges associated with waste management have underscored the importance of the circular economy—a framework designed to balance resource utilization, align production with consumption, and effectively address waste-related issues. Within this context, social complementary factors in waste management are crucial for overcoming these significant challenges. This study investigates the current state of solid waste management within the circular economy, with a particular focus on socio-cultural and behavioral dynamics, as well as the socio-technical implications of these processes. The research aims to elucidate the influence of socio-cultural dynamics on waste management practices. A comprehensive search of the Web of Science (WoS) database, using the keyword “solid waste management” up to August 10, 2024, yielded 33,247 relevant studies. To extract key themes from this extensive body of literature, Latent Dirichlet Allocation (LDA) topic modeling was applied. Subsequently, BERT-based deep learning methods were employed for text classification to identify which socio-cultural dynamics are most emphasized in the selected studies. The model achieved an overall accuracy of 87.5% in predicting the dominant topics. The findings from this study provide significant insights into the socio-cultural factors influencing waste management practices within the circular economy. By identifying these dynamics, the research contributes to a deeper understanding of how social and cultural factors can be leveraged to enhance the effectiveness of waste management strategies.

Keywords: Circular economy; Sustainability; Solid waste management; Socio-cultural dynamics; Deep learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:blg:msudev:v:16:y:2024:i:2:p:59-71:n:5

DOI: 10.54989/msd-2024-0015

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