We Didn't Miss You: Interpolating Missing Opinions

When mining user streams from social media, activity gaps are inevitable, which is known as the sparsity of user data. Such sparsity can significantly degrade the performance of a predictive system that relies on time-sensitive user content. To mitigate this issue, conventional approaches generally tend to discard periods with missing data. However, this solution leads to neglecting information generated by other users which, if utilized, could potentially enhance the quality of the predictive model. So the following question arises: is it possible to alleviate the impact of absent data while preserving the available content contributed within the same timespan? Despite the fact that this problem is well-known, it has not been thoroughly studied before. The goal of this work is to find a way of interpolating missing data from user's network and his previous activities. We investigate how different types of user profiles affect overall behavior predictability. Proposed models are evaluated on a case study of a micro-blogging system for the investment community.