Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter Optimisation
Junhao Song, Yingfang Yuan, Kaiwen Chang, Bing Xu, Jin Xuan, Wei Pang

TL;DR
This study uses advanced topic modelling with hyperparameter optimisation to analyze public attention on the circular economy across social media and news platforms, revealing key concerns and attention patterns.
Contribution
It introduces a novel twin hyperparameter optimisation framework for topic models applied to CE, providing more accurate insights into public discourse.
Findings
Public concerns focus on sustainability and recyclable materials.
The Guardian shows higher attention to CE topics than Twitter.
Insights inform policy and education strategies.
Abstract
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…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Computational and Text Analysis Methods · Digital Marketing and Social Media
MethodsSparse Evolutionary Training
