%0 Conference Proceedings
%T Consumer Insight on Driverless Automobile Technology Adoption via Twitter Data: A Sentiment Analytic Approach
%+ Tomas Bata University in Zlin (UTB)
%A Kwarteng, Michael, Adu
%A Ntsiful, Alex
%A Botchway, Raphael, Kwaku
%A Pilik, Michal
%A Oplatková, Zuzana, Komínková
%Z Part 6: Emerging Technologies in Consumer Decision Making and Choice
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B International Working Conference on Transfer and Diffusion of IT (TDIT)
%C Tiruchirappalli, India
%Y Sujeet K. Sharma
%Y Yogesh K. Dwivedi
%Y Bhimaraya Metri
%Y Nripendra P. Rana
%I Springer International Publishing
%3 Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation
%V AICT-617
%N Part I
%P 463-473
%8 2020-12-18
%D 2020
%R 10.1007/978-3-030-64849-7_41
%K Driverless vehicle
%K Twitter
%K Sentiment analysis
%K Autonomous
%K Technology
%K Innovation
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Technology has sped up the innovation effort in the automobile industry. Further to this automobile innovation such as intelligent climate control, adaptive cruise control, and others, we find in today’s vehicles, it has been predicted that by 2030, there will be driverless vehicles, of which samples are already on the market. The news and the sights of these so-called driverless vehicles have generated mixed reactions, and this motivated our study. Hence the present study focuses on a dataset of tweets associated with driverless vehicles downloaded using the Twitter API. Valence Aware Dictionary and sentiment Reasoner (VADER), a lexicon and rule-based sentiment analysis tool were used in extracting sentiments on the tweets to gauge public opinions about the acceptance and adoption of the driverless vehicles ahead of their launch. The VADER sentiment analysis results, however, show that the general discussion on driverless vehicles was positive. Besides, we generated a word cloud to visually analyze the terms in the dataset to gain further insights and understand the messages conveyed by the tweets in other to enhance the usage and adoption of driverless vehicles. This study will enable self-driving vehicle technology service providers and autonomous vehicle manufacturers to gain more insights on how to transform the transportation sector by investing in research and technology.
%G English
%Z TC 8
%Z WG 8.6
%2 https://inria.hal.science/hal-03701820/document
%2 https://inria.hal.science/hal-03701820/file/497052_1_En_41_Chapter.pdf
%L hal-03701820
%U https://inria.hal.science/hal-03701820
%~ SHS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC8
%~ IFIP-TDIT
%~ IFIP-WG8-6
%~ IFIP-AICT-617