A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models

TL;DR

This survey reviews recent quantum-cognitively inspired models for sentiment analysis, highlighting their theoretical foundations, innovative approaches, and potential for advancing understanding of sentiment through quantum probability concepts.

Contribution

It provides a comprehensive overview of the integration of quantum cognition and deep learning in sentiment analysis, emphasizing recent developments and future research directions.

Findings

Quantum models outperform classical models in certain sentiment tasks.

Quantum probability offers advantages in modeling cognitive uncertainty.

Emerging models show promising results in sentiment classification.

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.