A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy

Xue, Yangyimin; Kambhampati, Chandrasekhar; Cheng, Yongqiang; Mishra, Nishikant; Wulandhari, Nur; Deutz, Pauline

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.

Citation

Xue, Y., Kambhampati, C., Cheng, Y., Mishra, N., Wulandhari, N., & Deutz, P. (2024). A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy. International Journal of Computational Intelligence Systems, 17(1), Article 8. https://doi.org/10.1007/s44196-023-00375-7

• Journal Article Type: Article
• Acceptance Date: Nov 21, 2023
• Online Publication Date: Jan 10, 2024
• Publication Date: Dec 1, 2024
• Deposit Date: Jan 27, 2024
• Publicly Available Date: Jan 29, 2024
• Journal: International Journal of Computational Intelligence Systems
• Electronic ISSN: 1875-6883
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 1
• Article Number: 8
• DOI: https://doi.org/10.1007/s44196-023-00375-7
• Keywords: LDA; Model visualisation; Sentiment analysis; Comments’ classification
• Public URL: https://hull-repository.worktribe.com/output/4509333