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 related to circular products, and key public concerns. To achieve these objectives, we collected data from diverse platforms, including Twitter, Reddit, and The Guardian, a...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824000995 |
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| author | Junhao Song Yingfang Yuan Kaiwen Chang Bing Xu Jin Xuan Wei Pang |
| author_facet | Junhao Song Yingfang Yuan Kaiwen Chang Bing Xu Jin Xuan Wei Pang |
| author_sort | Junhao Song |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-82db3b807aa248b8a016b9f95c6699d2 |
| institution | DOAJ |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-82db3b807aa248b8a016b9f95c6699d22025-08-20T02:49:00ZengElsevierEnergy and AI2666-54682024-12-011810043310.1016/j.egyai.2024.100433Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisationJunhao Song0Yingfang Yuan1Kaiwen Chang2Bing Xu3Jin Xuan4Wei Pang5School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom; Faculty of Engineering, Imperial College London, London, United KingdomSchool of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United KingdomSchool of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaEdinburgh Business School, Heriot-Watt University, Edinburgh, United KingdomFaculty of Engineering and Physical Sciences, University of Surrey, Surrey, United KingdomSchool of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2666546824000995Circular economyPulic attentionTopic modellingMachine learningHyperparameter optimisation |
| spellingShingle | Junhao Song Yingfang Yuan Kaiwen Chang Bing Xu Jin Xuan Wei Pang Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation Energy and AI Circular economy Pulic attention Topic modelling Machine learning Hyperparameter optimisation |
| title | Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation |
| title_full | Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation |
| title_fullStr | Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation |
| title_full_unstemmed | Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation |
| title_short | Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation |
| title_sort | exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation |
| topic | Circular economy Pulic attention Topic modelling Machine learning Hyperparameter optimisation |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824000995 |
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