YouTube as a Source of Information in Understanding Autonomous Vehicle Consumers: Natural Language Processing Study

Subasish Das, Anandi Dutta, Tomas Lindheimer, Mohammad Jalayer, Zachary Elgart

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The automotive industry is currently experiencing a revolution with the advent and deployment of autonomous vehicles. Several countries are conducting large-scale testing of autonomous vehicles on private and even public roads. It is important to examine the attitudes and potential concerns of end users towards autonomous cars before mass deployment. To facilitate the transition to autonomous vehicles, the automotive industry produces many videos on its products and technologies. The largest video sharing website, YouTube.com, hosts many videos on autonomous vehicle technology. Content analysis and text mining of the comments related to the videos with large numbers of views can provide insight about potential end-user feedback. This study examines two questions: first, how do people view autonomous vehicles? Second, what polarities exist regarding (a) content and (b) automation level? The researchers found 107 videos on YouTube using a related keyword search and examined comments on the 15 most-viewed videos, which had a total of 60.9 million views and around 25,000 comments. The videos were manually clustered based on their content and automation level. This study used two natural language processing (NLP) tools to perform knowledge discovery from a bag of approximately seven million words. The key issues in the comment threads were mostly associated with efficiency, performance, trust, comfort, and safety. The perception of safety and risk increased in the textual contents when videos presented full automation level. Sentiment analysis shows mixed sentiments towards autonomous vehicle technologies, however, the positive sentiments were higher than the negative.

Original languageEnglish (US)
Pages (from-to)242-253
Number of pages12
JournalTransportation Research Record
Volume2673
Issue number8
DOIs
StatePublished - Aug 1 2019

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering

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