Abstract
With the growth of textual data and the simultaneous advancements in Text Analytics enabling the exploitation of this huge amount of unstructured data, companies are provided with the opportunity to tap into the previously hidden knowledge. However, how to use this valuable source, still is not unveiled for various domains, such as also for the transportation sector. Accordingly, this research aims at examining the potential of textual data in transportation. For this purpose, a case study was designed on public opinion towards the adoption of driverless cars. This case study was framed together with the Danish road directorate, which is, in this case, the problem owner. Traditionally, public opinion is often captured by means of surveys. However, this paper provides demonstrations in which public opinion towards the adoption of driverless cars is examined through the exploitation of newspaper articles and tweets using topic modelling, document classification and sentiment analysis. These analyses have for instance shown that Text Analytics may be a supplementary tool to surveys, since they may extract additional knowledge which may not be captured through the application of surveys. In this case, the Danish Road Directorate can use these result to supplement their strategies and expectations towards the adoption of driverless cars by incorporating the public’s opinion more carefully.
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Buch, R., Beheshti-Kashi, S., Nielsen, T.A.S., Kinra, A. (2018). Big Data Analytics: A Case Study of Public Opinion Towards the Adoption of Driverless Cars. In: Freitag, M., Kotzab, H., Pannek, J. (eds) Dynamics in Logistics. LDIC 2018. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-319-74225-0_47
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DOI: https://doi.org/10.1007/978-3-319-74225-0_47
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