Journal
PATTERNS
Volume 2, Issue 2, Pages -Publisher
CELL PRESS
DOI: 10.1016/j.patter.2020.100195
Keywords
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Categories
Funding
- National Science Foundation [1945332, 1931980]
- Microsoft Azure Sponsorship
- Ivan Allen College Dean's SGR-C Award
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1931980] Funding Source: National Science Foundation
- SBE Off Of Multidisciplinary Activities
- Direct For Social, Behav & Economic Scie [1945332] Funding Source: National Science Foundation
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This article utilizes deep learning technology to discover topics of attention in user reviews and applies it to public policy analysis and large-scale implementation, enhancing intelligence for the EV charging market.
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.
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