  Video sentiment analysis as a decision-making process is inherently complex,
involving the fusion of decisions from multiple modalities and the so-caused
cognitive biases. Inspired by recent advances in quantum cognition, we show
that the sentiment judgment from one modality could be incompatible with the
judgment from another, i.e., the order matters and they cannot be jointly
measured to produce a final decision. Thus the cognitive process exhibits
"quantum-like" biases that cannot be captured by classical probability
theories. Accordingly, we propose a fundamentally new, quantum cognitively
motivated fusion strategy for predicting sentiment judgments. In particular, we
formulate utterances as quantum superposition states of positive and negative
sentiment judgments, and uni-modal classifiers as mutually incompatible
observables, on a complex-valued Hilbert space with positive-operator valued
measures. Experiments on two benchmarking datasets illustrate that our model
significantly outperforms various existing decision level and a range of
state-of-the-art content-level fusion approaches. The results also show that
the concept of incompatibility allows effective handling of all combination
patterns, including those extreme cases that are wrongly predicted by all
uni-modal classifiers.
