To advance the circular economy (CE), it is crucial to gain insights into the evolution of public sentiments, cognitive pathways of the masses concerning circular products and digital technology, and recognise the primary concerns. To achieve this, we collected data related to the CE from diverse platforms including Twitter, Reddit, and The Guardian. This comprehensive data collection spanned across three distinct strata of the public: the general public, professionals, and official sources. Subsequently, we utilised three topic models on the collected data. Topic modelling represents a type of data-driven and machine learning approach for text mining, capable of automatically categorising a large number of documents into distinct semantic groups. Simultaneously, these groups are described by topics, and these topics can aid in understanding the semantic content of documents at a high level. However, the performance of topic modelling may vary depending on different hyperparameter values. Therefore, in this study, we proposed a framework for topic modelling with hyperparameter optimisation for CE and conducted a series of systematic experiments to ensure that topic models are set with appropriate hyperparameters and to gain insights into the correlations between the CE and public opinion based on well-established models. The results of this study indicate that concerns about sustainability and economic impact persist across all three datasets. Official sources demonstrate a higher level of engagement with the application and regulation of CE. To the best of our knowledge, this study is pioneering in investigating various levels of public opinions concerning CE through topic modelling with the exploration of hyperparameter optimisation.

翻译：为推进循环经济（CE）发展，深入了解公众情绪的演变、大众对循环产品及数字技术的认知路径，并识别主要关注点至关重要。为此，我们从Twitter、Reddit和《卫报》等多个平台收集了与循环经济相关的数据。这项综合数据采集覆盖了三个不同层次的公众群体：普通公众、专业人士及官方来源。随后，我们对收集的数据应用了三种主题模型。主题建模是一种基于数据驱动的机器学习文本挖掘方法，能够自动将大量文档归类为不同的语义集群，同时这些集群由主题描述，有助于从高层语义理解文档内容。然而，主题模型的性能可能因超参数值的不同而存在差异。因此，本研究提出了一种面向循环经济的超参数优化主题建模框架，并通过系统实验确保主题模型配置了合适的超参数，同时基于稳健模型深入理解循环经济与公众舆论之间的关联。研究结果表明，可持续性与经济影响相关的关注在所有三个数据集中均持续存在。官方来源在循环经济的应用与监管方面表现出更高参与度。据我们所知，本研究首次通过结合超参数优化的主题建模方法，系统探究了不同层次公众对循环经济的舆论观点。