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Topic Classification of Electric Vehicle Consumer Experiences with Transformer-Based Deep Learning

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Document pages: 43 pages

Abstract: The transportation sector is a major contributor to greenhouse gas (GHG) emissions and is a driver of adverse health effects globally. Increasingly, government policies have promoted the adoption of electric vehicles (EVs) as a solution to mitigate GHG emissions. However, government analysts have failed to fully utilize consumer data in decisions related to charging infrastructure. This is because a large share of EV data is unstructured text, which presents challenges for data discovery. In this article, we deploy advances in transformer-based deep learning to discover topics of attention in a nationally representative sample of user reviews. We report classification accuracies greater than 91 (F1 scores of 0.83), outperforming previously leading algorithms in this domain. We describe applications of these deep learning models for public policy analysis and large-scale implementation. This capability can boost intelligence for the EV charging market, which is expected to grow to $27.6 billion USD by 2027.Cite paper as: Ha, S., Marchetto, D. J., Dharur, S., & Asensio, O. I. (2021). Topic classification of electric vehicle consumer experiences with transformer-based deep learning. Patterns, 2(2), 100195. https: 10.1016 j.patter.2020.100195

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