This paper presents SentiQNN (Sentiment Quantum Neural Network), a novel quantum-inspired neural network architecture specifically designed for sentiment analysis tasks in natural language processing. SentiQNN leverages quantum computing principles including superposition, entanglement, and quantum measurement to enhance traditional neural network capabilities for text classification. The model incorporates complex-valued embeddings, quantum mixture operations, and multi-task learning with sentiment lexicon integration to achieve superior performance in sentiment classification tasks. Through extensive experimentation across multiple benchmark datasets, SentiQNN demonstrates state-of-the-art performance with 88.11% accuracy, outperforming traditional classical approaches by 3-7%. The architecture introduces innovative quantum state preparation layers, quantum circuit operations, and quantum measurement mechanisms specifically optimized for natural language understanding. Our comprehensive evaluation reveals that quantum-inspired approaches can significantly enhance sentiment analysis performance while maintaining computational efficiency. The findings contribute to the emerging field of quantum machine learning and provide a foundation for future research in quantum-enhanced natural language processing systems.
This paper presents SentiQNN (Sentiment Quantum Neural Network), a novel quantum-inspired neural network architecture specifically designed for sentiment analysis tasks in natural language processing. SentiQNN leverages quantum computing principles including superposition, entanglement, and quantum measurement to enhance traditional neural network capabilities for text classification. The model incorporates complex-valued embeddings, quantum mixture operations, and multi-task learning with sentiment lexicon integration to achieve superior performance in sentiment classification tasks. Through extensive experimentation across multiple benchmark datasets, SentiQNN demonstrates state-of-the-art performance with 88.11% accuracy, outperforming traditional classical approaches by 3-7%. The architecture introduces innovative quantum state preparation layers, quantum circuit operations, and quantum measurement mechanisms specifically optimized for natural language understanding. Our comprehensive evaluation reveals that quantum-inspired approaches can significantly enhance sentiment analysis performance while maintaining computational efficiency. The findings contribute to the emerging field of quantum machine learning and provide a foundation for future research in quantum-enhanced natural language processing systems.
Sentiment analysis aims to study, analyse and identify the sentiment polarity contained in subjective documents. In the realm of natural language processing (NLP), the study of sentiment analysis and its subtask research is a hot topic, which has very important significance. The existing sentiment analysis methods based on sentiment lexicon and machine learning take into account contextual semantic information, but these methods still lack the ability to utilize context information, so they cannot effectively encode context information. Inspired by the concept of density matrix in quantum mechanics, we propose a sentiment analysis method, named Complex-valued Quantum-enhanced Long Short-term Memory Neural Network (CQLSTM). It leverages complex-valued embedding to incorporate more semantic information and utilizes the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network for feature extraction. Specifically, a complex-valued neural network based on density matrix is used to capture interactions between words (i.e., the correlation between words). Additionally, the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network, which is inspired by the quantum measurement theory and quantum long short-term memory neural network, is developed to learn interactions between sentences (i.e., contextual semantic information). This approach effectively encodes semantic dependencies, which reflects the dispersion of words in the embedded space of sentences and comprehensively captures interactive information and long-term dependencies among the emotional features between words. Comparative experiments were performed on four sentiment analysis datasets using five traditional models, showcasing the effectiveness of the CQLSTM model.
Most of the existing quantum-inspired models are based on amplitude-phase embedding to model natural language, which maps words into Hilbert space. In quantum-computing theory, the vectors corresponding to quantum states are all complex values, so there is a gap between these two areas. Presently, complex-valued neural networks have been studied, but their practical applications are few, let alone in the downstream tasks of natural language processing such as sentiment analysis and language modeling. In fact, the complex-valued neural network can use the imaginary part information to embed hidden information and can express more complex information, which is suitable for modeling complex natural language. Meanwhile, quantum-inspired models are defined in Hilbert space, which is also a complex space. So it is natural to construct quantum-inspired models based on complex-valued neural networks. Therefore, we propose a new quantum-inspired model for NLP, ComplexQNN, which contains a complex-valued embedding layer, a quantum encoding layer, and a measurement layer. The modules of ComplexQNN are fully based on complex-valued neural networks. It is more in line with quantum-computing theory and easier to transfer to quantum computers in the future to achieve exponential acceleration. We conducted experiments on six sentiment-classification datasets comparing with five classical models (TextCNN, GRU, ELMo, BERT, and RoBERTa). The results show that our model has improved by 10% in accuracy metric compared with TextCNN and GRU, and has competitive experimental results with ELMo, BERT, and RoBERTa.
Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing. We focus on a dataset of tweets related to COVID-19, where the textual content is vectorized using TF-IDF and fed into both classical feedforward networks and hybrid architectures incorporating parameterized quantum circuits. Our results show that hybrid models can achieve accuracy comparable to the classical baseline, while exhibiting distinct learning dynamics, especially in terms of validation loss and accuracy, that suggest a richer representational capacity. Moreover, when applying transfer learning to an SMS spam classification task, the hybrid models consistently outperform the classical counterpart, achieving an accuracy increase of 15 percentage points (from 66% to 81%) on the spam class, demonstrating enhanced generalization. These findings highlight the feasibility of employing QML for natural language processing and point toward the potential advantages of hybrid models as quantum hardware continues to advance.
This research introduces a new paradigm for sentiment analysis on patient medication reviews through the application of quantum computing to improve machine learning models. A pre-existing dataset of medication reviews from the UCI Machine Learning repository was utilized, involving Quantum Natural Language Processing (QNLP) techniques, namely the application of quantum embeddings produced by quantum circuits to improve the performance of sentiment classification. Experimental findings indicate that improved models through quantum techniques, like the Quantum Random Forest, outperform conventional models in terms of accuracy and precision. These improvements are ascribed to quantum embeddings’ capacity to efficiently represent complex linguistic patterns in high-dimensional data. Additionally, the study uses robust validation techniques including performance benchmarking and cross-validation to guarantee reliability. Through a scalable and context-aware framework for natural language comprehension, this study highlights the revolutionary potential of QNLP in improving sentiment analysis in the healthcare industry.
Quantum computing has emerged as a breakthrough technology for sentiment and emotion analysis, providing computational efficiency and interpretability benefits. This paper compares three quantum-inspired methods: fully complex-valued neural networks, multimodal sentiment analysis models, and quantum affective computing. This paper compares three quantum-inspired methods: fully complex-valued neural networks, multimodal sentiment analysis models, and quantum affective computing models. The research uses six benchmark sentiment datasets (CR, MPQA, MR, SST-2, SUBJ, and SST-5) to compare the performance of the methods. The approach involves representing textual and multimodal data as complex quantum states, using quantum-inspired feature representations, and using quantum measurement methods for classification. The results show that the Complex Quantum Neural Network (QNN) performs better than baseline models such as BERT and RoBERTa, with the best F1-scores on all datasets. Specifically, it has an F1-score of 86 on CR, 86 on MPQA, 90 on MR, 88 on SST-2, 97 on SUBJ, and 53 on SST-5, outperforming baseline deep learning methods. These results show the potential of quantum computing to revolutionize sentiment analysis, providing more efficient and interpretable models with better contextual understanding and scalability.
As digital communication ecosystems continue to expand, brand management increasingly relies on the ability to extract, interpret, and predict consumer sentiment from large and dynamic data sources. Conventional sentiment analysis models—based on classical machine learning and deep neural architectures—often face limitations in handling ambiguity, contextual entanglement, and non-linear emotional expressions embedded in natural language. This study introduces a quantum-inspired sentiment analysis framework designed to enhance predictive brand management through improved semantic representation and probabilistic reasoning. Drawing inspiration from quantum mechanics, the proposed model employs principles such as superposition, entanglement, and Hilbert space embeddings to represent sentiment as a probabilistic distribution of cognitive states rather than as discrete classifications. By integrating these quantum-inspired techniques with classical natural language processing (NLP) and machine learning models, the system captures contextual subtleties and sentiment overlaps that traditional models often overlook. Experimental evaluation on social media and brand communication datasets demonstrates that quantum-inspired embeddings outperform conventional word vectorization techniques in recognizing sentiment polarity shifts and predicting brand reputation trends. The study highlights how quantum probabilistic reasoning can improve the interpretability and foresight of brand perception models, allowing organizations to proactively manage reputation, customer engagement, and market positioning.