Digital Discontent: A Longitudinal and Event-Driven Analysis of Public Sentiment Towards Hong Kong’s Taxi Industry (2009–2024)

Hong Kong's taxi industry, a vital component of its public transport network, has long been a subject of public debate and criticism. While official complaint statistics offer a formal measure of dissatisfaction, they often fail to capture the full spectrum and intensity of public opinion. This paper presents a large-scale, longitudinal sentiment analysis based on a substantial corpus of public social media data from 2009 to 2024, processed using advanced Large Language Models (LLMs). Our findings reveal four critical insights. First, there has been a chronic and significant escalation in negative sentiment over the past fifteen years, rising from 78% in 2009 to a staggering 95% in 2024, indicating a deep-seated systemic issue rather than isolated incidents. Second, a stark disparity exists in sentiment between different communities, with tourists exhibiting near-universal negative sentiment (99%) compared to residents (89%), highlighting the industry's detrimental impact on Hong Kong's international image. Third, event-driven analysis demonstrates that both industry actions (e.g., strikes) and government policy interventions consistently trigger sharp spikes in public negativity, suggesting a profound erosion of public trust in both the industry and its regulators. Fourth, the post-COVID era has witnessed a rapid resurgence of dissatisfaction, with key events such as a viral overcharging video and the passage of new fleet licensing legislation correlating with peak levels of negative sentiment. We conclude that the data points to a crisis of legitimacy for the incumbent taxi model. Incremental reforms have proven insufficient. We recommend a paradigm shift towards a more competitive, consumer-centric, and data-driven regulatory framework that embraces technology and addresses the distinct needs of both residents and tourists to restore public confidence.