Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter Optimisation
<|reference_start|>Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter Optimisation: To advance the circular economy (CE), it is crucial to gain insights into the evolution of public attention, cognitive pathways of the masses concerning circular products, and to identify primary concerns. To achieve this, 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, this research proposed a novel framework that integrates twin (single and multi-objective) hyperparameter optimisation for the CE. We conducted systematic experiments to ensure that topic models are set with appropriate hyperparameters under different constraints, providing valuable insights into the correlations between CE and public attention. In summary, our optimised model reveals that public remains concerned about the economic impacts of sustainability and circular practices, particularly regarding recyclable materials and environmentally sustainable technologies. The analysis shows that the CE has attracted significant attention on The Guardian, especially in topics related to sustainable development and environmental protection technologies, while discussions are comparatively less active on Twitter. These insights highlight the need for policymakers to implement targeted education programs, create incentives for businesses to adopt CE principles, and enforce more stringent waste management policies alongside improved recycling processes.<|reference_end|>
@article{song2024exploring,
    title={Exploring Public Attention in the Circular Economy through Topic
  Modelling with Twin Hyperparameter Optimisation},
    author={Junhao Song, Yingfang Yuan, Kaiwen Chang, Bing Xu, Jin Xuan, Wei Pang},
    journal={Energy and AI, Volume 18, December 2024, 100433},
    year={2024},
    doi={10.1016/j.egyai.2024.100433},
    archivePrefix={arXiv},
    eprint={2405.10452},
    primaryClass={cs.CL cs.LG}
}