  When people try to understand nuanced language they typically process
multiple input sensor modalities to complete this cognitive task. It turns out
the human brain has even a specialized neuron formation, called sagittal
stratum, to help us understand sarcasm. We use this biological formation as the
inspiration for designing a neural network architecture that combines
predictions of different models on the same text to construct robust, accurate
and computationally efficient classifiers for sentiment analysis and study
several different realizations. Among them, we propose a systematic new
approach to combining multiple predictions based on a dedicated neural network
and develop mathematical analysis of it along with state-of-the-art
experimental results. We also propose a heuristic-hybrid technique for
combining models and back it up with experimental results on a representative
benchmark dataset and comparisons to other methods to show the advantages of
the new approaches.
