Social- Semantic Analysis of Social Media Interactions to assess customer satisfaction in transit agencies

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The aim of this proposed study is to progress the discipline of social media analytics in transit through developing an integrated and interactive model with emphasis on the interplay between social and semantic (and their sentiment) network parameters. The aim is to provide transit agencies with means to understand the topics of debate or issues of concerns or interest by different groups of riders based on their interactions on Twitter and Facebook. That is: who is talking about what topics at different times? Do the topics discussed relate to recent events, communications, or changes to the transit system? This is essential to better profiling customers and their levels of satisfaction as a step towards understanding behavior patterns and drivers. Equally important, this is essential for effective communication and outreach that can help reduce the typically negative input by customers on social media. The results of the research will be used to build a customizable analytics portal in the form of an adaptive dashboard for use by Transit staff to analyze social media contents. It is expected that a staff member will start by choosing a social media outlet or account to analyze. The system will then allow the user to select several semantic and social parameters to study. The system will also allow the user to select and customize periodical reports to track trends. The use of machine learning in conducting such analysis can yield valuable insights to system planners and operators.