The development of foreign literature, embodiment of emotional value in modern, contemporary foreign literature is more attentive on experience, through reading it, one can understand humanistic, personal feelings embedded in the work and grasp the author’s personality, spiritual experience. To analyze sentiment data of foreign literary works, proposed Emotion Analysis of Literary Works Based on Qutrit-inspired Fully Self-supervised Quantum Neural Network Method (EA-LW- QIFSQN). Initially input data are collected from NLP-dataset. Afterward, the input data provided to preprocessing. In preprocessing segment Federated Neural Collaborative Filtering (FNCF) is used to clean the unwanted data. Then preprocessed data is fed to feature extraction, synchro spline-kernelled chirplet extracting transform (SKCET) is used to extract two features such as textual features and lexical features. Afterwards QIFSQN is used to classify the emotions likes joy, sadness, anger, fear. Generally, QIFSQN doesn’t show some optimization adaption techniques to determine optimum parameter to offer accurate detection. Polar Coordinate Bald Eagle Search Algorithm (PCBSOA) is proposed to enhance QICCN classifies the emotions accurately. The proposed technique is executed and efficacy of EA-LW- QIFSQN technique is assessed with support of numerous performances like accuracy, recall, precision and F1-scorce is analyzed. Then, performance of EA-LW- QIFSQN technique is analyzed with existing techniques like emotion analysis of literary works depend on attention mechanisms with fusion of two-channel features (EA-LW-CNN), integrative improvement of modern literary works with traditional culture combined by semantic association network modeling(EA-LW-SAN) and emotion expression in modern literary appreciation: emotion-depend analysis (EA-LW-RFA) respectively.
The development of foreign literature, embodiment of emotional value in modern, contemporary foreign literature is more attentive on experience, through reading it, one can understand humanistic, personal feelings embedded in the work and grasp the author’s personality, spiritual experience. To analyze sentiment data of foreign literary works, proposed Emotion Analysis of Literary Works Based on Qutrit-inspired Fully Self-supervised Quantum Neural Network Method (EA-LW- QIFSQN). Initially input data are collected from NLP-dataset. Afterward, the input data provided to preprocessing. In preprocessing segment Federated Neural Collaborative Filtering (FNCF) is used to clean the unwanted data. Then preprocessed data is fed to feature extraction, synchro spline-kernelled chirplet extracting transform (SKCET) is used to extract two features such as textual features and lexical features. Afterwards QIFSQN is used to classify the emotions likes joy, sadness, anger, fear. Generally, QIFSQN doesn’t show some optimization adaption techniques to determine optimum parameter to offer accurate detection. Polar Coordinate Bald Eagle Search Algorithm (PCBSOA) is proposed to enhance QICCN classifies the emotions accurately. The proposed technique is executed and efficacy of EA-LW- QIFSQN technique is assessed with support of numerous performances like accuracy, recall, precision and F1-scorce is analyzed. Then, performance of EA-LW- QIFSQN technique is analyzed with existing techniques like emotion analysis of literary works depend on attention mechanisms with fusion of two-channel features (EA-LW-CNN), integrative improvement of modern literary works with traditional culture combined by semantic association network modeling(EA-LW-SAN) and emotion expression in modern literary appreciation: emotion-depend analysis (EA-LW-RFA) respectively.
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.
Abstract This study explores Quantum AI’s potential in enhancing Speech Emotion Recognition (SER) systems. Our primary objective is to evaluate the performance of quantum-inspired algorithms compared to classical machine-learning approaches in accurately identifying and classifying emotions from speech signals. We hypothesise that quantum computing techniques can improve the efficiency and accuracy of emotion recognition, particularly in handling complex, highdimensional acoustic data. We developed a hybrid quantum-classical model that combines quantum-inspired neural networks with traditional feature extraction techniques. Our approach utilises quantum circuits for data encoding and quantum variational algorithms for classification. We implemented quantum versions of support vector machines (QSVM) and quantum approximate optimisation algorithms (QAOA) for emotion classification tasks. These quantum methods were compared against a hybrid Convolutional Neural Network and Long Short Term (LSTM). The hybrid network is called a Convolutional Long Short Term network(CLSTM). The study employed a custom dataset for speech emotion recognition. We prepossessed the audio data to extract relevant acoustic features, including mel-frequency cepstral coefficients (MFCCs), pitch, and energy. In contrast, our implemented quantum-inspired model for this corpus showed lower performance, with the highest training accuracy of 30%, struggling with most emotions but performing best with ’Trust’. These varied results across different implementations suggest that while Quantum AI techniques show promise in advancing speech emotion recognition, their effectiveness may depend on the specific dataset, language, and model architecture used. Finally, the speed of a simulated quantum network is shown to outperform current methods in terms of matrix multiplication by leaps and bounds.
Sentiment analysis (SA) is an essential component of natural language processing (NLP) and is 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 (ML) and neuro-fuzzy approaches to address the exponential growth of data and the complexity of language structures in SA. 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. In addition, they are frequently insensitive to input variations. In this article, we propose a novel hybrid approach for SA called the quantum fuzzy neural network (QFNN), which leverages quantum properties and incorporates a fuzzy layer to overcome the limitations of classical SA 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 show 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 SA on a large scale in a noisy environment. The proposed approach expedites sentiment data processing and precisely analyzes different forms of textual data, thus improving sentiment classification and insights associated with SA.
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.
Sentiment, emotion, and sarcasm analysis in multimodal dialogues is crucial for understanding the underlying intentions and attitudes expressed by individuals. Traditional methods often struggle to capture the full intensity of these polarities, leading to less accurate results. To address this limitation, we propose a quantum-inspired approach leveraging Quantum Neural Networks (QNNs) for enhanced classification and intensity analysis. A key component of our method is the Variational Quantum Eigensolver (VQE), a hybrid quantum-classical algorithm that optimizes the parameters of the QNN by minimizing the eigenvalues of a Hamiltonian system. This optimization enables the network to learn complex relationships in multimodal data more effectively. Our approach surpasses state-of-the-art methods, achieving up to 7.5 % higher accuracy and 6.8 % greater precision. Experiments on benchmark datasets such as MUStARD, Memotion, CMU-MOSEI, and MELD demonstrate its effectiveness, with an F1-score of 87.3 % on CMU-MOSEI. This method is particularly beneficial in domains like social media, customer support, and entertainment, where both verbal and non-verbal cues play a critical role in accurate sentiment analysis .
Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset.