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

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.

Factors Influencing and Contributing to Perceived Safety of Passengers during Driverless Shuttle Rides

“…In general, passengers felt safe across all test rides. This relates to research on what changes opinions about AVs: Although the public opinion about AVs might be skeptical, with safety concerns for passengers as well as for pedestrians being the biggest skepticisms [30,37,39,51,52], people's opinions and attitudes positively change when they experience a ride in an AV [22,53], and perceived safety increases with experience with a driverless shuttle [54], people would be also more willing to pay for a ride in a shuttle ride after having experienced it [55]. In our study, passengers indicated after their experience with the autonomous shuttle that their perception of road safety would not need the shuttle to have a separated lane when they thought about a future where autonomous shuttles are already part of normal traffic.…”

Consumer Acceptance in Measuring Greece’s Readiness for Transport Automation

“…At worldwide level, numerous studies have been undertaken to measure the acceptance of AV, either in psychological terms, investigating the feeling of safety (or unsafety) the users experience in AVs [5,6] showing a rather reluctant attitude of older and less technologically savvy people to use fully automated vehicles, while acceptance is rather higher and with less variability when it comes to lower levels of automation-or in terms of their market penetration potential and the users' willingness to have/willingness to pay (WTH/WTP) [7,8]-indicating a higher interest and WTP from young, experienced and higher-income drivers, living in urban areas. In addition, studies based on social media data sentiment analysis have been used to identify fears and restrictions of users towards autonomous mobility [9]. Of course, other parameters also significantly affect user acceptance, like social influence, individual differences, and system characteristics [10], which include also geographical and cultural features that affect the personalities and market potential.…”

Investigating social media spatiotemporal transferability for transport

“…Chaniotakis et al (2016) have provided a comprehensive review of the directions that transportation-related Social Media research is positioned. In short, the directions that the literature takes are either the use of Social Media for modeling and forecasting purposes, including an aspect of the use of Social Media data for OD Estimation (Liao et al, 2021;Osorio-Arjona and García-Palomares, 2019), Attraction Models (Lee et al, 2019;Yang et al, 2018;Hu and Jin, 2018), activity modelling (Cui et al, 2018;Chaniotakis et al, 2017;Hasan and Ukkusuri, 2018;Lee et al, 2016), extraction of mobility-related and spatial characteristics (Ebrahimpour et al, 2020;Hu et al, 2020;Kim et al, 2018;Yao et al, 2018;Yang et al, 2019) transportation-related sentiment analysis (Rahman et al, 2021;Bakalos et al, 2020;Sari et al, 2019;Ali et al, 2018Ali et al, , 2019, prediction and event detection (Chaturvedi et al, 2021;Yao and Qian, 2021;Alomari et al, 2019Alomari et al, , 2021Zulfikar et al, 2019;Zhang et al, 2018;Xu et al, 2018;Pereira et al, 2015), and accessibility analysis with the complementary use of Twitter data (Kim and Lee, 2021;Qian et al, 2020;Moyano et al, 2018). On another perspective, social media have also been used mainly from transport providers, for the direct communication that their platform allow with the end users (National Academies of Sciences, Engineering, and Medicine, 2021).…”