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.
Highlights
Autonomous transportation modes have been a popular field of research, as increasing the autonomy of vehicles has a vast impact on everyday life, as well as the way that a lot of current business is conducted
It is worth mentioning here that normally, confidence score is not used in such a way. This is because high probability of assigning a post in a class depicts the confidence of the machine learning model to assign a specific label and not a more positive/negative opinion
The current paper presents 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
Summary
Autonomous transportation modes have been a popular field of research, as increasing the autonomy of vehicles has a vast impact on everyday life, as well as the way that a lot of current business is conducted. The introduction of a fully automated and autonomous transportation system can mimic the revolution of the early 20th century and its introduction of motorized vehicles that allowed the transportation of large loads in a faster and more efficient manner [1] This promised revolution needs to overcome one major obstacle, beyond the technical barriers, to be established as an integral part of modern life, user acceptance. The recruitment approaches in such focus groups present difficulties on their own [2,3] To this end, a novel popular mode of measuring public opinion has been the analysis of social media data, which allows the integration of social theories with computational methods, as “Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from social media data” [4].
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