survey

A Survey of Quantum-cognitively Inspired Sentiment Analysis Models

Authors:

Dawei SongAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 1

Article No.: 15, Pages 1 - 37

https://doi.org/10.1145/3604550

Published: 26 August 2023 Publication History

Abstract

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.

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Cited By

Yao BTiwari PLi Q(2025)Self-supervised pre-trained neural network for quantum natural language processingNeural Networks10.1016/j.neunet.2024.107004184(107004)Online publication date: Apr-2025
https://doi.org/10.1016/j.neunet.2024.107004
Zhang CSu ZLi QSong DTiwari P(2025)Quantum-inspired neural network with hierarchical entanglement embedding for matchingNeural Networks10.1016/j.neunet.2024.106915182(106915)Online publication date: Feb-2025
https://doi.org/10.1016/j.neunet.2024.106915
Huynh LHong JMian ASuzuki HCamtepe S(2024)Intrusion Detection Systems Using Quantum-Inspired Density Matrix Encodings2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)10.1109/DSN-W60302.2024.00019(32-38)Online publication date: 24-Jun-2024
https://doi.org/10.1109/DSN-W60302.2024.00019
Show More Cited By

Index Terms

A Survey of Quantum-cognitively Inspired Sentiment Analysis Models
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 1

January 2024

918 pages

EISSN:1557-7341

DOI:10.1145/3613490

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 26 August 2023

Online AM: 13 June 2023

Accepted: 31 May 2023

Revised: 29 April 2023

Received: 28 April 2022

Published in CSUR Volume 56, Issue 1

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Cited By

Yao BTiwari PLi Q(2025)Self-supervised pre-trained neural network for quantum natural language processingNeural Networks10.1016/j.neunet.2024.107004184(107004)Online publication date: Apr-2025
https://doi.org/10.1016/j.neunet.2024.107004
Zhang CSu ZLi QSong DTiwari P(2025)Quantum-inspired neural network with hierarchical entanglement embedding for matchingNeural Networks10.1016/j.neunet.2024.106915182(106915)Online publication date: Feb-2025
https://doi.org/10.1016/j.neunet.2024.106915
Huynh LHong JMian ASuzuki HCamtepe S(2024)Intrusion Detection Systems Using Quantum-Inspired Density Matrix Encodings2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)10.1109/DSN-W60302.2024.00019(32-38)Online publication date: 24-Jun-2024
https://doi.org/10.1109/DSN-W60302.2024.00019
Fan MKong MWang XHao FZhang C(2024)FITE-GAT: Enhancing aspect-level sentiment classification with FT-RoBERTa induced trees and graph attention networkExpert Systems with Applications10.1016/j.eswa.2024.125890(125890)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.125890
Lak ABoostani RAlenizi FMohammed AFakhrahmad S(2024)RoBERTa, ResNeXt and BiLSTM with self-attentionApplied Soft Computing10.1016/j.asoc.2024.112018164:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112018
Yan FIliyasu ALi NSalama AHirota K(2024)Quantum robotics: a review of emerging trendsQuantum Machine Intelligence10.1007/s42484-024-00225-56:2Online publication date: 28-Nov-2024
https://doi.org/10.1007/s42484-024-00225-5

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