In recent years, the number of people embarking on leisure-travel trips has increased rapidly. Consequently, travel patterns have changed depending on the type of leisure trip. However, accurately establishing non-daily travel data using conventional travel diary surveys with the existing self-survey method is impossible. A possible solution is the use of social media, which is widely used, as a data source. Users upload various posts describing their leisure travels on social media, which include both structured data, such as posting date and location information, and unstructured data, such as content and images. The purpose of this study was to analyze the relationship between individual behavior and green transportation during leisure travel using location-based social media data. Social media data were collected using a web crawler and annual transportation data were collected from the green transportation promotion areas in Seoul, Korea. A text mining technique was applied to the content of the posts to analyze the types of leisure for each individual. The text data in the contents were classified in detail by applying latent Dirichlet allocation (LDA), a topic modeling technique. Subsequently, to identify individual travel behavior, spatial analysis was performed by comparing the location information of the post with the usage of various transportation by leisure type. Consequently, four leisure types, i.e., exercise, tourism, rest, and social, were identified to be prevalent in the northern, central, southern, and eastern regions. Social and tourism-type leisure activities involved the use of shared bicycles and public transport. In contrast, people pursuing exercise-type leisure activities tended not to use shared bicycles and public transportation. The results of this study indicate that there is a correlation between activity type and choice of transportation. Identifying the purpose of travel can help strengthen the connection with green transportation.