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Exploration of the Dynamic Evolution of Online Public Opinion towards Waste Classification in Shanghai

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  • Yingxia Xue

    (Management Science and Engineering, School of Economics and Management, Tongji University, 4800 Caoan Rd., Shanghai 201804, China)

  • Honglei Liu

    (Department of Construction Management, Changshu Institute of Technology, Changshu 215500, China)

Abstract

Shanghai is one of the fastest-growing metropolises and the first city in China to implement mandatory waste classification. Waste classification policy of Shanghai has attracted widespread attention since its implementation in July 2019. However, previous papers have not focused on online public attitudes surrounding the implementation of a waste classification policy in Shanghai. In order to fill this gap, this paper explored the dynamic evolution of online public attitudes towards waste classification in Shanghai by using sentiment analysis technology and topic modeling technology. It was found that the proportion of negative posts each month was about 20%; therefore, online public sentiment towards waste classification in Shanghai was generally positive. Compared with the first three months of policy implementation, the public sentiment towards Shanghai’s waste classification became more positive, with the exception of two special periods. Negative posts in July 2019 mainly discussed waste’s environmental hazards and policy provisions. New topics in negative posts in later months focused on some specific problems, including the process of throwing away wet waste, the allocated throwing times, the number of waste cans, takeaway meal disposal, and gathering activities. Improving the factors causing the negative sentiments in the posts will help the government better implement the policy. The paper will help the government to receive higher public support for the waste classification policy in Shanghai. The present findings also have great reference significance for other cities.

Suggested Citation

  • Yingxia Xue & Honglei Liu, 2023. "Exploration of the Dynamic Evolution of Online Public Opinion towards Waste Classification in Shanghai," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1471-:d:1034778
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    References listed on IDEAS

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    5. Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.
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