Analytical Framework to Understand Electric Vehicle Adoption by Leveraging Sentiment Analysis - Myneni, Madhu Bala; Akkineni, Haritha; Mai, Cherukuri Kiran; Boppana, Sisira (Journal of Mobile Multimedia, 2024)
Electric vehicles (EVs) are gaining eminence as a sustainable alternative to conventional vehicles. Even though EV’s are more expensive than conventional vehicles, people are excited about this green initiative. Hence, understanding public sentiment towards them becomes crucial for industry stakeholders and policymakers. This paper proposes a Twitter-based analytical framework to develop the application of sentiment analysis to understand public perceptions and concerns toward EVs. The opinions are tagged with three categories: constructive(positive), adverse(negative), and unbiased(neutral) from the overall public perception of electric mobility. It has been implemented in two phased manner as descriptive and predictive analytics on Twitter data. The study provides insights into the public’s support, concerns, and potential barriers to EV adoption. A sentiment model was evaluated with various machine-learning algorithms. The results ascertained that the SVM is performing well among all other models with 89% accuracy. Findings highlight critical factors influencing perception and offer recommendations for addressing public concerns to encourage broader acceptance of electric vehicles.