Public attitudes and sentiments towards new energy vehicles in China: A text mining approach

Zezhou Wu, Qiufeng He*, Jiarun Li, Guoqiang Bi, Maxwell Fordjour Antwi-Afari

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Public attitudes and sentiments are considered important factors driving the development of new energy vehicles (NEVs) in China, yet few studies have effectively articulated the public attitudes and sentiments on a national scale. Social media platforms provide open and large-scale data on how the public views NEVs. Accordingly, based on data collected from Sina Weibo, the largest open social media platform in China, this study adopts web crawler and text mining to explore public attitudes and sentiments toward NEVs. The results show that the majority of NEVs followers belong to the media and broadcasting unit, which may lead to exaggerated advertising of NEVs performance. Public pay more attention to NEVs in environmental pollution and developed economies regions than others. In addition, it is found that the public is generally concerned about driving range, policies, battery charging and safety performance, and NEVs-related attributes. Some interesting findings are that preferential policies such as unrestricted driving are more popular with the public than tax incentives, and the public attaches a high degree of importance to the brand in the attributes of NEVs. Moreover, the Chinese public has relatively positive sentiments on NEVs, mainly due to the impact of China's carbon reduction targets. Negative sentiments are mainly due to safety and battery technology issues. Finally, some policy implications derived from the discussion are given.
Original languageEnglish
Article number113242
Pages (from-to)113242
Number of pages12
JournalRenewable and sustainable energy reviews
Early online date16 Mar 2023
Publication statusPublished - May 2023

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China [grant number 72001148 ]; the Foundation for Basic and Applied Basic Research of Guangdong Province [grant number 2019A1515110247 ]; and the Research Start-up Funding in Shenzhen [grant number 000376 ].

Publisher Copyright:
© 2023 Elsevier Ltd


  • new energy vehicles
  • public attitude
  • sentiment analysis
  • text mining
  • China


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