Impact of COVID-19 pandemic on ride-hailing services based on large-scale Twitter data analysis

Abstract

Ride-hailing services have gained popularity in recent years due to attributes such as reduced travel costs, traffic congestion, and emissions. However, with the impact of COVID-19, the ride-hailing market is estimated to lose its fair share of an uprising as a transportation mode. During normal and critical circumstances, ride-hailing service users express their concerns, habits, and emotions through posting on social platforms such as Twitter. Hence, Twitter, as an emerging data source, is an effective and innovative digital platform to observe the rider’s behavior in ride-hailing services. This study hydrates large-scale Twitter reactions related to shared mobility to perform comparative sentiment and emotion analysis to understand the impact of COVID-19 on transportation network services in pre-pandemic and during pandemic conditions. Amid pandemic, negative tweets (34%) associated with ‘sad’ (15%) and ‘anger’ (15%) emotions were most prevalent in the dataset.

Keywords

• Ride-hailing
• TNC
• Sentiment analysis
• Emotion detection
• Twitter data
• COVID-19