Sentiment analysis is an essential component of natural language processing, used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. Researchers have developed various classical machine learning and neuro-fuzzy approaches to address the exponential growth of data and the complexity of language structures in sentiment analysis. However, these approaches often fail to determine the optimal number of clusters, interpret results accurately, handle noise or outliers efficiently, and scale effectively to high-dimensional data. Additionally, they are frequently insensitive to input variations. In this paper, we propose a novel hybrid approach for sentiment analysis called the Quantum Fuzzy Neural Network (QFNN), which leverages quantum properties and incorporates a fuzzy layer to overcome the limitations of classical sentiment analysis algorithms. In this study, we test the proposed approach on two Twitter datasets: the Coronavirus Tweets Dataset (CVTD) and the General Sentimental Tweets Dataset (GSTD), and compare it with classical and hybrid algorithms. The results demonstrate that QFNN outperforms all classical, quantum, and hybrid algorithms, achieving 100% and 90% accuracy in the case of CVTD and GSTD, respectively. Furthermore, QFNN demonstrates its robustness against six different noise models, providing the potential to tackle the computational complexity associated with sentiment analysis on a large scale in a noisy environment. The proposed approach expedites sentiment data processing and precisely analyses different forms of textual data, thereby enhancing sentiment classification and insights associated with sentiment analysis.

翻译：情感分析是自然语言处理的关键组成部分，用于分析不同语境下的情感、态度与情绪基调。它为公众舆论、客户反馈与用户体验提供了重要洞察。针对情感分析中数据量的指数级增长与语言结构的复杂性，研究者已开发了多种经典机器学习与神经模糊方法。然而，这些方法常难以确定最优聚类数量、准确解释结果、高效处理噪声或异常值，并难以有效扩展至高维数据。此外，它们对输入变化往往不够敏感。本文提出一种名为量子模糊神经网络的新型混合情感分析方法，该方法利用量子特性并引入模糊层以克服经典情感分析算法的局限性。本研究在两个Twitter数据集（冠状病毒推文数据集与通用情感推文数据集）上测试了所提方法，并与经典及混合算法进行了对比。结果表明，QFNN在所有经典、量子及混合算法中表现最优，在两个数据集上分别达到了100%与90%的准确率。此外，QFNN在六种不同噪声模型下均表现出强鲁棒性，展现了在噪声环境中大规模处理情感分析相关计算复杂度的潜力。所提方法能加速情感数据处理过程，精准分析不同形式的文本数据，从而提升情感分类效果及相关洞察力。