In this paper, we consider the problem of latent sentiment detection in Online Social Networks such as Twitter. Modeling the underlying social network as an Ising prior, we demonstrate the effect that the underlying social network structure has on the performance of a trivial sentiment detector. In doing so, we introduce a novel communications-oriented framework for characterizing the probability of error and the associated error exponent, based on information theoretic analysis. We study the variation of the calculated error exponent for several stylized network topologies such as the complete network, the star network and the closed-chain network, and show the importance of the network structure in determining detection performance.
