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.

翻译：量子理论最初作为描述微观粒子运动的物理理论而被提出，现已广泛应用于涉及人类认知与决策的各种非物理领域，这些领域具有内在的不确定性并展现出非经典、类量子的特征。情绪分析正是这类领域的典型范例。近年来，通过利用量子概率（一种源于量子力学方法论的非经典概率）的建模能力与深度神经网络，一系列新颖的量子认知启发情绪分析模型应运而生并取得了优异表现。本综述对这一跨学科迷人领域的最新发展进行了及时概述。我们首先在理论层面介绍量子概率与量子认知的背景，分析它们相较于经典理论在建模情绪分析认知方面的优势；随后详细阐述并讨论近年来的量子认知启发模型，重点关注这些模型如何应对情绪分析任务中的核心挑战；最后探讨当前研究的局限性并展望未来研究方向。