Topic classification of electric vehicle consumer experiences with transformer-based deep learning

Patterns

Daniel J. Marchetto¹  Sooji Ha²  Omar I. Asensio³  Sameer Dharur⁴ 

[1] School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA;School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;School of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA;School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30332, USA;

关键词: natural language processing;    deep learning;    transformers;    consumer behavior;    electric vehicles;    policy analysis;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: 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 US$27.6 billion by 2027. The Bigger Picture: Transformer neural networks have emerged as the preeminent models for natural language processing, seeing production-level use with Google search and translation algorithms. These models have had a major impact on context learning from text in many fields, e.g., health care, finance, manufacturing; however, there have been no empirical advances to date in electric mobility.Given the digital transformations in energy and transportation, there are growing opportunities for real-time analysis of critical energy infrastructure. A large, untapped source of EV mobility data is unstructured text generated by mobile app users reviewing charging stations. Using transformer-based deep learning, we present multilabel classification of charging station reviews with performance exceeding human experts in some cases. This paves the way for automatic discovery and real-time tracking of EV user experiences, which can inform local and regional policies to address climate change.

【 授权许可】

Unknown