With the advent of big data, there has been a growing interest in exploring the relationship between crowdsourced (subjective) road safety data collected from social media platforms, such as how road users perceive safety, and objective road safety data based on collision events. This is because subjective data could facilitate traditional road safety analysis based on collision data if their relationship is known. Additionally, measuring subjective safety is important because users’ perceptions of safety can have adverse effects on their mobility and influence the decisions made by road agencies when developing policies, ultimately impacting the occurrence of road collisions. Recently, the Authors developed a tool for collecting and analyzing crowdsourced road safety data from Twitter (now known as X) using natural language processing and machine learning techniques. This tool can extract, classify, and conduct sentiment analysis of public road safety-related tweets. The aim of this paper is to present a comparative analysis between road safety-related tweets collected and classified with this tool and collision rates. Also, the strength of the correlation between these two (subjective and objective) dimensions was measured. Road safety-related tweets were collected for Vancouver Area between the years 2017 and 2019 and compared to collision data for different forward sortation areas (FSAs). A reasonable and significant correlation was observed for some categories of classified road safety-related tweets and collision rates. Further exploration of these relationships could enable the utilization of this tool as an alternative or supplementary approach to traditional analyses based solely on collision data.