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Public perception of AI: The analysis of sentiment towards apollo go based on AG-BCLK model

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  • Wu, Ji'an
  • Chen, Hong
  • Ma, Wanqi

Abstract

The advent of AI has resulted in a surge in autonomous vehicle technologies in the transportation sector. This research investigates public sentiment in China concerning autonomous taxi technology. A cross-platform, multi-source dataset (AGNet) and a multi-task sentiment-attention classification algorithm (AG-BCLK) are developed to analyze popular acceptance across two dimensions: emotional attitude and public concern. The experimental findings indicate that the AG-BCLK model surpasses other deep learning models in processing text data, with a classification accuracy of 92.5 % for sentiment labels and 86.4 % for attention labels. The study indicated that online comments initially exhibited positive sentiment; but, when adverse public opinion emerged, the proportion of negative sentiment gradually increased, leading to a more negative overall tone in the remarks. Nevertheless, participants in the preliminary testing areas exhibited relatively positive emotional dispositions. Informed by the outcomes of public concern clustering, strategic recommendations are presented for the government to enhance oversight, for enterprises to bolster safety management, and for individuals to proactively adjust to technological advancements, thereby facilitating the robust development of technology.

Suggested Citation

  • Wu, Ji'an & Chen, Hong & Ma, Wanqi, 2025. "Public perception of AI: The analysis of sentiment towards apollo go based on AG-BCLK model," Transport Policy, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:trapol:v:174:y:2025:i:c:s0967070x25003993
    DOI: 10.1016/j.tranpol.2025.103856
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