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.
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.
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.
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.
Predicting the stock market has been an uphill battle with the non-linear, intricate, and highly unpredictable nature of the market. Classic machine learning and deep learning techniques have performed but lack scalability, computational cost, and nuance in capturing market patterns. Quantum computing (QC) has been a game-changing instrument for dealing with massive financial data and tuning predictive models for the past several years. Here, we are demonstrating the application of quantum machine learning (QML) methods, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum Boltzmann machines (QBM), to stock market prediction. Employing superposition, entanglement, and parallelism in quantum computation, we will improve prediction precision, improve feature optimisation, and strengthen trend analysis of the market. Our approach utilises quantum-enhanced financial modelling to handle large volumes of historical stock data and sentiment analysis within a high-dimensional Hilbert space. The findings show that quantum models surpass classical models in capturing market trends, computation time reduction, and managing intricate correlations. Although quantum hardware is in its nascent stage, this research proves the promise of quantum computing as a disruptor in financial forecasting.
Understanding people's emotions is becoming increasingly important in diverse areas such as social media, e-commerce, and brand or trend analysis. While both deep learning networks and traditional machine learning techniques, such as Support Vector Machines (SVM) have their uses, they often fail to efficiently handle very big or complicated datasets. The study of Quantum Machine Learning (QML) methods could help solve these problems. Support Vector Machine (SVM) quantum algorithms are a scalable way to work with large amounts of data. They are more accurate at classifying datasets than traditional SVM while using less computing power. We suggest a mixed model that blends QSVM and CNN for deep learning and quantum processing that work together. The method includes extracting features, classifying mood, and preparing data from two standard sets: Amazon reviews of goods and IMDb reviews of movies. As shown in experiments on a large dataset, the suggested QMLbased hybrid design method is up to 12 % more accurate and nearly 20 % faster when working than standard machine learning methods. The new development makes quantum computing useful for faster and more flexible data processing to help with mood classification in areas like brand intelligence, watching social media, and reviewing customer reviews.
Sentiment analysis (SA) is a critical component of Natural Language Processing (NLP), particularly for automatic text classification. However, previous approaches have shortcomings in capturing nuances such as negation, word pairings, and contextual understanding, particularly in languages like Hindi. To solve these issues, this research offers a novel Quantum Neural Network (QNN) technique designed specifically for sentiment analysis in Hindi text data, which employs quantum computing concepts to capture linguistic nuances and context better. This study uses a Quantum Variational Auto Encoder to encode classical data into a quantum form, capturing diverse sentiments like sarcasm and colloquial expressions. A Tuned Quantum Convolutional Neural Network architecture is introduced to capture complex linguistic syntax. A novel Sequential-based hyperband optimization technique is used to enhance model performance. The hybrid approach significantly improves accuracy and efficiency in handling quantum data, contributing to the advancement of SA, particularly in Hindi Movie Reviews. The findings demonstrate that the proposed strategy performs best with accuracy 97.64 %, precision 85.93%, recall 99.17%, F-1 score 92.20%, than other accepted strategies.