  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.
