A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy
Abstract The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the efficiency of this process. Social media has...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-01-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00375-7 |
| _version_ | 1797355491230220288 |
|---|---|
| author | Yangyimin Xue Chandrasekhar Kambhampati Yongqiang Cheng Nishikant Mishra Nur Wulandhari Pauline Deutz |
| author_facet | Yangyimin Xue Chandrasekhar Kambhampati Yongqiang Cheng Nishikant Mishra Nur Wulandhari Pauline Deutz |
| author_sort | Yangyimin Xue |
| collection | DOAJ |
| description | Abstract The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the efficiency of this process. Social media has become the main ways for the public to obtain information and express opinions and feelings. This motivated us to mine the perceptions and behavioral responses towards plastic usage using social media data. In this paper, we proposed a framework for data collection and analysis based on mainstream media in the UK to obtain public opinions on plastics. An unsupervised machine learning model based on Latent Dirichlet Allocation (LDA) has been employed to analyse and cluster the topics to deal with the lack of annotation of the data contents. An additional dictionary method was then proposed to evaluate the sentiment of the comments. The framework also provides tools to visualise the model and results to stimulate insightful understandings. We validated the framework's effectiveness by applying it to analyse three mainstream social media, where 6 first-level topic categories and 13 second-level topic categories from the comment texts related to plastics have been identified. The results show that public sentiment towards plastic products is generally stable. The spatiotemporal distribution of each topic's sentiment is highly correlated with the number of occurrences. |
| first_indexed | 2024-03-08T14:11:56Z |
| format | Article |
| id | doaj.art-2c756a01dc9a4ffc96b14c6cb7b03736 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-08T14:11:56Z |
| publishDate | 2024-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-2c756a01dc9a4ffc96b14c6cb7b037362024-01-14T12:36:01ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832024-01-0117111410.1007/s44196-023-00375-7A LDA-Based Social Media Data Mining Framework for Plastic Circular EconomyYangyimin Xue0Chandrasekhar Kambhampati1Yongqiang Cheng2Nishikant Mishra3Nur Wulandhari4Pauline Deutz5Department of Computer Science and Technology, University of HullDepartment of Computer Science and Technology, University of HullDepartment of Computer Science and Technology, University of HullBusiness School, University of HullBusiness School, University of HullDepartment of Geography, Geology and Environment, University of HullAbstract The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the efficiency of this process. Social media has become the main ways for the public to obtain information and express opinions and feelings. This motivated us to mine the perceptions and behavioral responses towards plastic usage using social media data. In this paper, we proposed a framework for data collection and analysis based on mainstream media in the UK to obtain public opinions on plastics. An unsupervised machine learning model based on Latent Dirichlet Allocation (LDA) has been employed to analyse and cluster the topics to deal with the lack of annotation of the data contents. An additional dictionary method was then proposed to evaluate the sentiment of the comments. The framework also provides tools to visualise the model and results to stimulate insightful understandings. We validated the framework's effectiveness by applying it to analyse three mainstream social media, where 6 first-level topic categories and 13 second-level topic categories from the comment texts related to plastics have been identified. The results show that public sentiment towards plastic products is generally stable. The spatiotemporal distribution of each topic's sentiment is highly correlated with the number of occurrences.https://doi.org/10.1007/s44196-023-00375-7LDAModel visualisationSentiment analysisComments’ classification |
| spellingShingle | Yangyimin Xue Chandrasekhar Kambhampati Yongqiang Cheng Nishikant Mishra Nur Wulandhari Pauline Deutz A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy International Journal of Computational Intelligence Systems LDA Model visualisation Sentiment analysis Comments’ classification |
| title | A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy |
| title_full | A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy |
| title_fullStr | A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy |
| title_full_unstemmed | A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy |
| title_short | A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy |
| title_sort | lda based social media data mining framework for plastic circular economy |
| topic | LDA Model visualisation Sentiment analysis Comments’ classification |
| url | https://doi.org/10.1007/s44196-023-00375-7 |
| work_keys_str_mv | AT yangyiminxue aldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT chandrasekharkambhampati aldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT yongqiangcheng aldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT nishikantmishra aldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT nurwulandhari aldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT paulinedeutz aldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT yangyiminxue ldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT chandrasekharkambhampati ldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT yongqiangcheng ldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT nishikantmishra ldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT nurwulandhari ldabasedsocialmediadataminingframeworkforplasticcirculareconomy AT paulinedeutz ldabasedsocialmediadataminingframeworkforplasticcirculareconomy |