A pricing mechanism using social media and web data to infer dynamic consumer valuations

The tides of sentiments expressed in online social media rise and fall. In recent years, the availability of big data has afforded researchers the ability to develop and evaluate techniques that allow us to identify, classify, aggregate, and even predict the sentiment dynamics for nearly any topic [1], [2]. The users of online social media platforms like Twitter are able to create, propagate, and consume information pertaining to any conceivable topic, and in doing so, they influence each other´s opinions and behavior. Herding behavior and online sentiment are mutually reinforcing, and have been shown to influence consumer purchasing decisions [3], [4].