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Crowdsensing and analyzing micro-event tweets for public transportation insights

Efficient and commuter friendly public transportation system is a critical part of a thriving and sustainable city. As cities experience fast growing resident population, their public transportation systems will have to cope with more demands for improvements. In this paper, we propose a crowdsensing and analysis framework to gather and analyze realtime commuter feedback from Twitter. We perform a series of text mining tasks identifying those feedback comments capturing bus related micro-events; extracting relevant entities; and, predicting event and sentiment labels. We conduct a series of experiments involving more than 14K labeled tweets. The experiments show that incorporating domain knowledge or domain specific labeled data into text analysis methods improves the accuracies of the above tasks. We further apply the tasks on nearly 200M public tweets from Singapore over a six month period to show that interesting insights about bus services and bus events can be derived in a scalable manner.