Utilizing the Twitter social media to identify transportation-related grievances in Indian cities

Due to population growth and rapid urbanization in Indian cities, transportation has evolved as a critical concern affecting a large number of commuters everyday. Hence it is important for the urban planners, policymakers, and transportation authorities of India to know about the different public grievances/concerns regarding transportation. This study aims to uncover valuable information about specific transport-related complaints/grievances in Indian cities from the vast pool of user-generated content on social media platforms such as Twitter. As an initial step, we have explored the broad sentiment of commuters in six Indian metropolitan cities about the existing transportation systems, and created a dataset that broadly classify tweets into negative and positive sentiments. Next, we have identified a set of fine-grained complaints/grievances in these tweets, and thus created the first dataset containing transport-related tweets labelled into various specific complaints/grievances in a multi-label setting. To our knowledge, there is no existing dataset that labels tweets according to specific concerns raised in the posts. We apply several classification models on the dataset, for classifying transportation-related tweets into the specific complaints/grievances. We further conducted a city-wise analysis to better comprehend the specific transport-related complaints prevalent in each Indian city.