# A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models

arXiv: https://arxiv.org/abs/2306.03608 · HTML: https://mlsanity.com/paper/2306.03608/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 145 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 28 of 28)

- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [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)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities](https://mlsanity.com/paper/1812.07809/) (2018)
- [A high-bias, low-variance introduction to Machine Learning for physicists](https://mlsanity.com/paper/1803.08823/) (2018)
- [QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer](https://mlsanity.com/paper/2102.12846/) (2021)
- [Foundations for Near-Term Quantum Natural Language Processing](https://mlsanity.com/paper/2012.03755/) (2020)
- [Quantum Natural Language Processing on Near-Term Quantum Computers](https://mlsanity.com/paper/2005.04147/) (2020)
- [A Transformer-based approach to Irony and Sarcasm detection](https://mlsanity.com/paper/1911.10401/) (2019)
- [Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design](https://mlsanity.com/paper/1704.01552/) (2017)
- [Classification with Quantum Machine Learning: A Survey](https://mlsanity.com/paper/2006.12270/) (2020)
- [The world as a neural network](https://mlsanity.com/paper/2008.01540/) (2020)
- [Quantum Language Model with Entanglement Embedding for Question Answering](https://mlsanity.com/paper/2008.09943/) (2020)
- [The Role of Conversation Context for Sarcasm Detection in Online Interactions](https://mlsanity.com/paper/1707.06226/) (2017)
- [Mean-field inference methods for neural networks](https://mlsanity.com/paper/1911.00890/) (2019)
- [Quantum-inspired Multimodal Fusion for Video Sentiment Analysis](https://mlsanity.com/paper/2103.10572/) (2021)
- [A Survey of Quantum Theory Inspired Approaches to Information Retrieval](https://mlsanity.com/paper/2007.04357/) (2020)
- [A Quantum Many-body Wave Function Inspired Language Modeling Approach](https://mlsanity.com/paper/1808.09891/) (2018)
- [Quantum-inspired Complex Word Embedding](https://mlsanity.com/paper/1805.11351/) (2018)
- [Semantic Hilbert Space for Text Representation Learning](https://mlsanity.com/paper/1902.09802/) (2019)
- [Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis](https://mlsanity.com/paper/2101.04406/) (2021)
- [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)
- [Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels](https://mlsanity.com/paper/2212.01744/) (2022)
- [Modelling Dynamic Interactions between Relevance Dimensions](https://mlsanity.com/paper/1907.10943/) (2019)

## Built on by (most cited first, 1 of 1)

- [SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems](https://mlsanity.com/paper/2412.12731/) (2024)

## Abstract (arXiv)

Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics. Sentiment analysis is a typical example of such domains. In the last few years, by leveraging the modeling power of quantum probability (a non-classical probability stemming from quantum mechanics methodology) and deep neural networks, a range of novel quantum-cognitively inspired models for sentiment analysis have emerged and performed well. This survey presents a timely overview of the latest developments in this fascinating cross-disciplinary area. We first provide a background of quantum probability and quantum cognition at a theoretical level, analyzing their advantages over classical theories in modeling the cognitive aspects of sentiment analysis. Then, recent quantum-cognitively inspired models are introduced and discussed in detail, focusing on how they approach the key challenges of the sentiment analysis task. Finally, we discuss the limitations of the current research and highlight future research directions.