This article proposes an LSTM-based approach for sarcasm detection by forming quantum features from textual data. In the proposed method, words are first encoded in quantum space, and their semantic similarity is calculated using interference. Based on the encoded data, quantum TF-IDF features are generated and fed into a neural network for classification. The obtained results were compared with those of the classical approach. According to experimental results, the quantum approach achieved 83% accuracy, while the classical approach achieved 78%. Additionally, the quantum approach reduced computation time by 49% compared to the classical one. Due to its superposition and parallelism properties, the quantum approach provides higher performance. Its limitations include testing on a simulator, interdependence between data, and the inability to use real quantum technologies. Applying quantum computing through TF-IDF methods for natural language processing and sarcasm detection can be a promising direction.
This article proposes an LSTM-based approach for sarcasm detection by forming quantum features from textual data. In the proposed method, words are first encoded in quantum space, and their semantic similarity is calculated using interference. Based on the encoded data, quantum TF-IDF features are generated and fed into a neural network for classification. The obtained results were compared with those of the classical approach. According to experimental results, the quantum approach achieved 83% accuracy, while the classical approach achieved 78%. Additionally, the quantum approach reduced computation time by 49% compared to the classical one. Due to its superposition and parallelism properties, the quantum approach provides higher performance. Its limitations include testing on a simulator, interdependence between data, and the inability to use real quantum technologies. Applying quantum computing through TF-IDF methods for natural language processing and sarcasm detection can be a promising direction.
This article proposes classical and quantum vectorization methods for text data classification, along with corresponding neural network architectures for classification based on these representations. The study applies classical and quantum variants of one-hot encoding, bag-of-words, TF-IDF, and word2vec vectorization techniques, which are respectively integrated with CNN, LSTM, MLP, and BiLSTM neural network models for text classification. All models are trained on the same dataset under identical experimental conditions, with classification accuracy and computational efficiency adopted as the primary evaluation metrics. Classical vectorization methods represent semantic and contextual properties of text in a limited manner, whereas quantum vectorization overcomes these limitations. When neural networks are trained using quantum-based representations, higher classification accuracy and significantly reduced computation time are achieved compared to classical approaches. The combined use of quantum word2vec and the BiLSTM architecture yields the highest performance among all evaluated models. The obtained results demonstrate that this approach is the most effective for text data classification, as it accurately captures semantic relationships and leverages deep neural network architectures. This study may serve as a scientific foundation for future research aimed at developing more advanced neural architectures based on quantum computing paradigms.
Sarcasm detection in unimodal or multimodal setting is a very complex task. Sarcasm, emotion, and sentiment are related to each other, and hence any multitask model could be an effective way to leverage the interdependence among these tasks. In order to better represent these clandestine associations, we avoid solely relying on traditional machine learning methods to encode the relationships between the modalities. In this article, we propose a hybrid quantum model that banks upon the low computational complexity and robust representational power of a variational quantum circuit (VQC) and the tried and tested dense neural network to tackle sentiment, emotion, and sarcasm classification simultaneously. We empirically establish that the quantum properties like superposition, entanglement, and interference will better capture and replicate not only the cross-modal interactions between text, acoustics, and visuals but also the correlations between the three responses. We consider the extended MUStARD dataset to evaluate our proposed hybrid model. The results show that our proposed hybrid quantum framework yields more promising results for the primary task of sarcasm detection with the help of the two secondary classification tasks,viz.sentiment and emotion.
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.
Sentence classification is a core task in Natural Language Processing, and sentiment analysis in particular remains difficult because of contextual ambiguity, semantic complexity, and the overlap between closely related sentiment classes. This paper presents a hybrid quantum-classical architecture for sentence classification that combines classical deep learning with quantum feature extraction. The classical branch employs a Feedforward Neural Network (FNN) and a Long Short-Term Memory (LSTM) network to learn complementary semantic and contextual representations, while the quantum branch reduces TF-IDF features through Truncated Singular Value Decomposition (SVD) and encodes them into a parameterized quantum circuit built with Qiskit. The classical and quantum representations are combined through feature-level fusion and classified using a MultiLayer Perceptron (MLP). The framework is evaluated on the SST-2 dataset for binary sentiment classification and on a merged SST-2 + SST-5 dataset for multi-class classification. The proposed hybrid model achieves an accuracy of 90.5%, precision of 89.9%, recall of 90.1%, and F1-score of 90.1% on SST-2, with a ROC-AUC close to 0.97, exceeding the individual FNN and LSTM baselines. On the merged multi-class dataset, the hybrid model attains 66.65% accuracy against 65.15% for FNN and 62.65% for LSTM. These results indicate that feature-level integration of classical and quantum representations can improve sentence classification performance within currently available quantum simulation environments.
Abstract Sarcasm detection is a challenging task in Natural Language Processing (NLP) due to its reliance on subtle and implicit linguistic cues that are often not explicitly expressed in text. Designing models capable of capturing such nuanced patterns remains an open problem. In this work, we investigate hybrid quantum–classical recurrent architectures for sarcasm detection by conducting a comparative study of classical and quantum-enhanced sequence models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and their quantum counterparts, Quantum LSTM (QLSTM) and Quantum GRU (QGRU).We employ a pre-trained BERT model to extract contextualized token embeddings, which are subsequently compressed through classical neural layers to ensure compatibility with Noisy Intermediate-Scale Quantum (NISQ) devices. In the proposed quantum-enhanced models, classical linear transformations within recurrent gates are replaced by Variational Quantum Circuits (VQCs), enabling the exploitation of quantum properties such as superposition and entanglement within a hybrid learning framework.Experimental results on a balanced sarcasm-labeled dataset indicate that both QLSTM and QGRU achieve performance levels comparable to their classical counterparts, despite utilizing significantly fewer trainable parameters. Notably, the quantum-enhanced models require approximately 10% of the parameters used by their classical equivalents while maintaining similar macro F1 scores. These findings suggest that hybrid quantum recurrent architectures can serve as compact and parameter-efficient alternatives to classical sequence models, highlighting their potential applicability in quantum natural language processing under near-term hardware constraints.
Quantum computers has the capacity to enhance the speed of intricate calculations, achieve parallelism via superpositions, and effectively process vast amounts of data. In addition, the domain of natural language processing (NLP) is swiftly drawing the attention of academics and engineers that want to construct more extensive computational models for NLP. Hence, the use of quantum technology in NLP endeavours, particularly in sentiment categorization, has promising prospects for further advancement. In this research work, a detailed analysis of different sentiment classification methods are investigated that are interpreted with quantum computing models. To be precise the Quantum Long Short Term Memory (QLSTM) and Quantum Support Vector Machines (QSVM) are examined to determine the efficiency among the models. The results are simulated in Qiskit simulator and real time quantum computer Qiskit_ibm_osaka to evaluate the examined models. The results shows the significance of the experimentation and it is proven using Accuracy, precision and recall as performance metrices. On an average, in terms of accuracy the proposed model outperforms existing methods with 8.9%, On precision the proposed model outperforms 11.1% compared to existing methods. And with respect to recall the proposed model outperforms 6.7% compared to existing methods.