Analytical Framework to Understand Electric Vehicle Adoption by Leveraging Sentiment Analysis

Authors

  • Madhu Bala Myneni Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India
  • Haritha Akkineni Department of Information Technology, PVP Siddhartha Institute of Technology, Andhra Pradesh, India
  • Cherukuri Kiran Mai Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India
  • Sisira Boppana North Carolina State University, USA

DOI:

https://doi.org/10.13052/jmm1550-4646.2054

Keywords:

Natural language processing, social media, logistic regression, K-nearest neighbour, support vector machine

Abstract

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.

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Author Biographies

Madhu Bala Myneni, Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India

Madhu Bala Myneni is working as a professor of computer science and engineering at VNR Vignana Jyothi Institute of Engineering and Technology (VNRVJIET), Hyderabad. She received her Ph.D. in Computer Science and Engineering from JNTUH. She has Twenty-one years of academic and research experience. Her research interests are Data Science frameworks, Image Mining, Text mining, Machine learning, Artificial Intelligence, Deep Learning, and Data Analytics. She has published 57 articles in reputed Journals indexed in SCOPUS, SCI, etc. She has published 2 patents. She received a research grant of 50 Lakhs from DST and AICTE. She is the Principal Investigator of a DST-funded sustainable smart city development project. And has received various grants from AICTE for organizing Short Term Training Programs; Infrastructure Development; and Faculty Development Programs. And selected a part of AICTE national mission programs such as Student Learning Outcomes Assessment (SLA); and Technical Book Writing (TBW). She is a reviewer for Elsevier, Springer, and more indexed journals. She acted as session chair, organizing member, and advisory member for various International Conferences. She delivered various invited talks on Data Modelling, Data Science, and Analytics. She is a Life member of professional bodies like CSI, ISTE, IEEE, WIE & International Association IAENG, ICST, and SDIWC.

Haritha Akkineni, Department of Information Technology, PVP Siddhartha Institute of Technology, Andhra Pradesh, India

Haritha Akkineni is an Associate Professor in Information Technology at PVP Siddhartha Institute of Technology, Vijayawada. She earned her Ph.D. in Computer Science and Engineering and has sixteen years of experience in academia and research. Her research interests include Data Science, Opinion Mining, Image Mining, Artificial Intelligence, Data Analytics, Deep Learning, and Machine Learning. She has published approximately 48 papers in reputed journals, including those indexed in SCI, SCOPUS, and UGC. She has authored a book on Opinion Mining and has published three patents, with one of them granted. She has received grants from AICTE to organize Short-Term Training Programs and serves as a reviewer for SCOPUS-indexed journals. Additionally, she has chaired workshops and tutorials at various international conferences and delivered several invited talks.

Cherukuri Kiran Mai, Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India

Cherukuri Kiran Mai, working as Professor in the Department of Computer Science, VNR VJ IET. She has 28 years of teaching, 7 years industry and 10 years of Research experience. She published 46 papers in various reputed National and International journals. She was awarded as “Best teacher in Computer Science” in the year 2010, by the professional body, International Society for Technology in Education (ISTE). She was on the Editorial Board of two Springer series – Learning and Analytics in Intelligent Systems, Machine Learning Technologies and applications during the year 2021 and 2022. The Proceedings of International Conference on Advances in Computer Engineering and Communication Systems, published by Atlantis Press – Springer Nature, Dr. C Kiran Mai was the Chief Editor during the year 2023. She conducted International Conference on Advances in Computer Engineering and Communication Systems (ICACECS) for three consecutive years. Her areas of Interests are communications, Data engineering and Block Chain Technologies.

Sisira Boppana, North Carolina State University, USA

Sisira Boppana is currently pursuing her Bachelor of Science at NC State University, USA. Her areas of interest are Artificial Intelligence, Pack Robotics and Ariel Robotics.

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Published

2024-12-20

How to Cite

Myneni, M. B. ., Akkineni, H. ., Mai, C. K. ., & Boppana, S. . (2024). Analytical Framework to Understand Electric Vehicle Adoption by Leveraging Sentiment Analysis. Journal of Mobile Multimedia, 20(05), 1067–1088. https://doi.org/10.13052/jmm1550-4646.2054

Issue

2024: Vol 20 Iss 5

Section

Articles