How are sentiments on autonomous vehicles influenced? An analysis using Twitter feeds
•This study proposes a machine learning framework to analyze public sentiments.•Sentiment bias caused by different terms of AV are detected.•Sentiment variations are explained referring to social events.•Public concerns are summarized, and possible policy insights are discussed. Public opinion on au...
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| Published in: | Transportation research. Part C, Emerging technologies Emerging technologies, 2021-10, Vol.131, p.103356, Article 103356 |
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| Main Authors: | , , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | •This study proposes a machine learning framework to analyze public sentiments.•Sentiment bias caused by different terms of AV are detected.•Sentiment variations are explained referring to social events.•Public concerns are summarized, and possible policy insights are discussed.
Public opinion on autonomous vehicles (AVs) is an important topic as AVs are expected to change the transportation system dramatically. Although the topic has been discussed extensively by many researchers and experts, it is beneficial to complement current studies by explaining the AV-related sentiment variations leveraging the unique social media features.
Different from traditional survey-based studies, this study relies on tweets to understand the sentiments on AVs. A comprehensive model framework is proposed to categorize sentiments, recognize critical dates with sentiment changes, and explain sentiment variations. The results indicate that the general sentiment is positive towards AVs, but social media users have sentiment biases towards different AV terms. Significant sentiment changes are often linked with major social events related to AVs, and the general public is more sensitive to social events than the most active users.
A wide range of policy insights are discussed based on the results of the analyses, including policies related to safety, pricing, unemployment, and AV adoption rate. Possible challenges and the corresponding strategies are also discussed. These insights will be helpful for the public agencies, automobile manufacturers, and technology companies in gaining a better understanding of AV adoption, and in preparing for the future of transportation systems. |
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| ISSN: | 0968-090X 1879-2359 |