# Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis

arXiv: https://arxiv.org/abs/2101.04406 · HTML: https://mlsanity.com/paper/2101.04406/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Efficient Low-rank Multimodal Fusion with Modality-Specific Factors](https://mlsanity.com/paper/1806.00064/) (2018)
- [MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos](https://mlsanity.com/paper/1606.06259/) (2016)
- [Memory Fusion Network for Multi-view Sequential Learning](https://mlsanity.com/paper/1802.00927/) (2018)
- [Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities](https://mlsanity.com/paper/1812.07809/) (2018)
- [Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors](https://mlsanity.com/paper/1811.09362/) (2018)
- [Multimodal Language Analysis with Recurrent Multistage Fusion](https://mlsanity.com/paper/1808.03920/) (2018)
- [Majority Vote of Diverse Classifiers for Late Fusion](https://mlsanity.com/paper/1404.7796/) (2014)
- [CNM: An Interpretable Complex-valued Network for Matching](https://mlsanity.com/paper/1904.05298/) (2019)
- [A Survey of Quantum Theory Inspired Approaches to Information Retrieval](https://mlsanity.com/paper/2007.04357/) (2020)
- [Modality-based Factorization for Multimodal Fusion](https://mlsanity.com/paper/1811.12624/) (2018)
- [Semantic Hilbert Space for Text Representation Learning](https://mlsanity.com/paper/1902.09802/) (2019)
- [Investigating non-classical correlations between decision fused multi-modal documents](https://mlsanity.com/paper/1810.11303/) (2018)
- [An Experimental Protocol to Derive and Validate a Quantum Model of Decision-Making](https://mlsanity.com/paper/1908.07935/) (2019)
- [Quantum-like Structure in Multidimensional Relevance Judgements](https://mlsanity.com/paper/2001.07075/) (2020)

## Built on by (most cited first, 4 of 4)

- [Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2411.13378/) (2024)
- [Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications](https://mlsanity.com/paper/2504.09909/) (2025)
- [A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models](https://mlsanity.com/paper/2306.03608/) (2023)
- [Quantum Visual Word Sense Disambiguation: Unraveling Ambiguities Through Quantum Inference Model](https://mlsanity.com/paper/2512.24687/) (2025)

## Abstract (arXiv)

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.