Consumer insight on driverless automobile technology adoption via twitter data: A sentiment analytic approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F20%3A63526218" target="_blank" >RIV/70883521:28120/20:63526218 - isvavai.cz</a>

Alternative codes found

RIV/70883521:28140/20:63526218

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-64849-7_41" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-64849-7_41</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-64849-7_41" target="_blank" >10.1007/978-3-030-64849-7_41</a>

Result language

angličtina

Original language name

Consumer insight on driverless automobile technology adoption via twitter data: A sentiment analytic approach

Original language description

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. © 2020, IFIP International Federation for Information Processing.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

50204 - Business and management

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation

ISBN

978-3-030-64860-2

ISSN

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e-ISSN

—

Number of pages

11

Pages from-to

463-473

Publisher name

Springer

Place of publication

Cham

Event location

Tiruchirappalli

Event date

Dec 17, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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