Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis

Authors

  • Dimitris Gkoumas The Open University, Milton Keynes, UK
  • Qiuchi Li University of Padua, Padua, Italy
  • Shahram Dehdashti Queensland University of Technology, Brisbane, Australia
  • Massimo Melucci University of Padua, Padua, Italy
  • Yijun Yu The Open University, Milton Keynes, UK
  • Dawei Song The Open University, Milton Keynes, UK

DOI:

https://doi.org/10.1609/aaai.v35i1.16165

Keywords:

Affective Computing

Abstract

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.

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Published

2021-05-18

How to Cite

Gkoumas, D., Li, Q., Dehdashti, S., Melucci, M., Yu, Y., & Song, D. (2021). Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 827-835. https://doi.org/10.1609/aaai.v35i1.16165

Issue

Section

AAAI Technical Track on Cognitive Modeling and Cognitive Systems