Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Junhao Song , Yingfang Yuan , Kaiwen Chang , Bing Xu , Jin Xuan , Wei Pang
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Abstract

To advance the circular economy (CE), it is crucial to gain insights into the evolution of public attention, cognitive pathways related to circular products, and key public concerns. To achieve these objectives, we collected data from diverse platforms, including Twitter, Reddit, and The Guardian, and utilised three topic models to analyse the data. Given the performance of topic modelling may vary depending on hyperparameter settings, we proposed a novel framework that integrates twin (single- and multi-objective) hyperparameter optimisation for CE analysis. Systematic experiments were conducted to determine appropriate hyperparameters under different constraints, providing valuable insights into the correlations between CE and public attention. Our findings reveal that economic implications of sustainability and circular practices, particularly around recyclable materials and environmentally sustainable technologies, remain a significant public concern. Topics related to sustainable development and environmental protection technologies are particularly prominent on The Guardian, while Twitter discussions are comparatively sparse. These insights highlight the importance of targeted education programmes, business incentives adopt CE practices, and stringent waste management policies alongside improved recycling processes.

Abstract Image

通过双参数优化的主题建模探索循环经济中的公众关注度
为了推动循环经济(CE)的发展,深入了解公众注意力的演变、与循环产品相关的认知路径以及公众的主要关注点至关重要。为了实现这些目标,我们从 Twitter、Reddit 和《卫报》等不同平台收集数据,并利用三种话题模型对数据进行分析。鉴于话题模型的性能可能因超参数设置的不同而有所差异,我们提出了一个新颖的框架,将双(单目标和多目标)超参数优化整合到消费电子分析中。我们进行了系统性实验,以确定不同约束条件下的适当超参数,从而为了解行政首长协调会与公众关注度之间的相关性提供了有价值的见解。我们的研究结果表明,可持续发展和循环实践的经济影响,特别是围绕可回收材料和环境可持续技术的经济影响,仍然是公众关注的一个重要问题。与可持续发展和环保技术相关的话题在《卫报》上尤为突出,而推特上的讨论则相对稀少。这些见解凸显了有针对性的教育计划、商业激励措施、严格的废物管理政策以及改进的回收流程的重要性。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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