Sentiment Analysis of Autonomous Vehicles After Extreme Events Using Social Media Data

Abstract

This paper aims to leverage social media data to understand the public opinion on autonomous driving after extreme events, including the Uber and Tesla crashes and the COVID-19 pandemic. Uber and Tesla crashes that happened consecutively in 2018 have posed uncertainty and the public concern toward the autonomous vehicle (AV) technology. The COVID-19 pandemic has drastically increased people's fear of taking mass transit, while the social distancing policy could easily favor contactless travel experiences provided by AVs. To understand people's attitudinal changes before and after these extreme events, three sources of social media data are leveraged: Facebook, Twitter and Reddit. Sentiment analysis is performed with BERT (Bidirectional Encoder Representation from Transformers) model to study the change in people's attitude toward AVs. Results show that after Uber and Tesla crashes, the proportion of people with a negative attitude increases, while after the pandemic, the proportion of people with a positive attitude increases. These results are quite consistent with our intuition. We then conduct regression analysis using XGBoost to analyze the impact of individual's demographic information on his/her sentiment toward AVs. We find that Age has the most significant effect on people's attitudes toward AVs. Engineers and entrepreneurs are more likely to introduce and discuss the AV technology in social media.