Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data

Nikolaos Bakalos, Nikolaos Papadakis and Antonios Litke
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Nikolaos Bakalos: Survey Engineering, National Technical University of Athens, Zografou Campus 9, Iroon Polytechniou str, Zografou, 15780 Athens, Greece
Nikolaos Papadakis: Research and Innovation, Infili Technologies PC, 60 Kousidi st, 15772 Athens, Greece
Antonios Litke: Research and Innovation, Infili Technologies PC, 60 Kousidi st, 15772 Athens, Greece

Logistics, 2020, vol. 4, issue 2, 1-14

Abstract: The purpose of this article is to present a framework for capturing and analyzing social media posts using a sentiment analysis tool to determine the views of the general public towards autonomous mobility. The paper presents the systems used and the results of this analysis, which was performed on social media posts from Twitter and Reddit. To achieve this, a specialized lexicon of terms was used to query social media content from the dedicated application programming interfaces (APIs) that the aforementioned social media platforms provide. The captured posts were then analyzed using a sentiment analysis framework, developed using state-of-the-art deep machine learning (ML) models. This framework provides labeling for the captured posts based on their content (i.e., classifies them as positive or negative opinions). The results of this classification were used to identify fears and autonomous mobility aspects that affect negative opinions. This method can provide a more realistic view of the general public’s perception of automated mobility, as it has the ability to analyze thousands of opinions and encapsulate the users’ opinion in a semi-automated way.

Keywords: sentiment analysis; acceptance of autonomous mobility; machine learning; social media mining (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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