SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems
Kshitij Dave, Nouhaila Innan, Bikash K. Behera, Zahid Mumtaz, Saif Al-Kuwari, Ahmed Farouk
TL;DR
The paper tackles sentiment analysis in the face of noisy, high-dimensional textual data by introducing SentiQNF, a Quantum Fuzzy Neural Network that fuses quantum encoding with a fuzzy layer. It formalizes a learning framework where TF-IDF features are embedded into quantum states, augmented with fuzzy reasoning to capture ambiguity, and optimized using ADAM with a mean-squared error objective. Extensive experiments on two Twitter datasets show QFNN achieving 100% accuracy on CVTD and 90% on GSTD, with strong robustness across six noise models, outperforming classical, quantum, and hybrid baselines. The work suggests significant practical impact for scalable, noise-tolerant sentiment analysis, while noting limitations such as binary labeling and the need for hardware-level error mitigation for deployment on quantum devices.
Abstract
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.