The employment of quantum computing in the sentiment analysis seems to provide remarkable accuracy and efficiency in classifying the positive and negative sentiments. The traditional methods find it difficult to deal with large volume of data which requires high training time. Hence, quantum computing holds an advantage over traditional methodologies. In the proposed Senti-Quan model the extracted sentiment scores are encoded into a quantum circuit. Various quantum gates like Hadamard, Pauli-X, CNOT, Rotation gates are implemented to get the detailed pattern of the data. The measured quantum results when compared with the actual sentiment provide an increased F1 score than other models working on Sentiment140 dataset without any requirement of training time. The average processing time of Senti-Quan model comes out to be 27 times faster than the other traditional techniques
The employment of quantum computing in the sentiment analysis seems to provide remarkable accuracy and efficiency in classifying the positive and negative sentiments. The traditional methods find it difficult to deal with large volume of data which requires high training time. Hence, quantum computing holds an advantage over traditional methodologies. In the proposed Senti-Quan model the extracted sentiment scores are encoded into a quantum circuit. Various quantum gates like Hadamard, Pauli-X, CNOT, Rotation gates are implemented to get the detailed pattern of the data. The measured quantum results when compared with the actual sentiment provide an increased F1 score than other models working on Sentiment140 dataset without any requirement of training time. The average processing time of Senti-Quan model comes out to be 27 times faster than the other traditional techniques
Sentiment analysis finds widespread applications in health, marketing, finance, stock markets, media, and politics. To analyze attitudes and emotions in textual data, handling large datasets and significant computational power is essential. Traditional computing methods struggle with the growing data volume, prompting interest in quantum computing as a promising alternative with its inherent high-speed processing capacity. This study focuses on sentiment analysis applied to texts derived from bilateral conversation dialogues. The primary objective is to categorize emotions within the text as positive, neutral, or negative, while concurrently identifying the speaker. To achieve this, a novel quantum-classical hybrid approach is proposed. The quantum side of this approach includes the variational quantum circuit (VQC). On the classical side, preprocessing of the data set, feature extraction with a model containing LSTM, and optimizing the parameters of VQC are performed. The proposed approach was trained and tested using a data set containing bilateral conversations. As a result of the tests, the proposed approach achieved a higher accuracy rate compared to studies using the classical approach. Thus, the effectiveness of the proposed approach is confirmed.
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.
With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. However, there is a lack of research investigating the performance of these techniques on the Arabic language. This paper presents a comparative study of quantum computing and machine learning for two datasets of Arabic language document classification. In the first dataset of 213,465 Arabic tweets, both classic machine learning (ML) and quantum computing approaches achieve high accuracy in sentiment analysis, with quantum computing slightly outperforming classic ML. Quantum computing completes the task in approximately 59 min, slightly faster than classic ML, which takes around 1 h. The precision, recall, and F1 score metrics indicate the effectiveness of both approaches in predicting sentiment in Arabic tweets. Classic ML achieves precision, recall, and F1 score values of 0.8215, 0.8175, and 0.8121, respectively, while quantum computing achieves values of 0.8239, 0.8199, and 0.8147, respectively. In the second dataset of 44,000 tweets, both classic ML (using the Random Forest algorithm) and quantum computing demonstrate significantly reduced processing times compared to the first dataset, with no substantial difference between them. Classic ML completes the analysis in approximately 2 min, while quantum computing takes approximately 1 min and 53 s. The accuracy of classic ML is higher at 0.9241 compared to 0.9205 for quantum computing. However, both approaches achieve high precision, recall, and F1 scores, indicating their effectiveness in accurately predicting sentiment in the dataset. Classic ML achieves precision, recall, and F1 score values of 0.9286, 0.9241, and 0.9249, respectively, while quantum computing achieves values of 0.92456, 0.9205, and 0.9214, respectively. The analysis of the metrics indicates that quantum computing approaches are effective in identifying positive instances and capturing relevant sentiment information in large datasets. On the other hand, traditional machine learning techniques exhibit faster processing times when dealing with smaller dataset sizes. This study provides valuable insights into the strengths and limitations of quantum computing and machine learning for Arabic document classification, emphasizing the potential of quantum computing in achieving high accuracy, particularly in scenarios where traditional machine learning techniques may encounter difficulties. These findings contribute to the development of more accurate and efficient document classification systems for Arabic data.
The collaboration between quantum computing and classical machine learning offers potential advantages in natural language processing, particularly in the sentiment analysis of human emotions and opinions expressed in large-scale datasets. In this work, we propose a methodology for sentiment analysis using hybrid quantum-classical machine learning algorithms. We investigate quantum kernel approaches and variational quantum circuit-based classifiers and integrate them with classical dimension reduction techniques such as PCA and Haar wavelet transform. The proposed methodology is evaluated using two distinct datasets, based on English and Bengali languages. Experimental results show that after dimensionality reduction of the data, performance of the quantum-based hybrid algorithms were consistent and better than classical methods.
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.
This research delves into the realm of sentiment analysis applied to Twitter data, utilizing quantum computing to advance its accuracy and efficiency. The escalating complexity and abundance of textual content on social media platforms have presented challenges for conventional computational methods in effectively gauging sentiments. Quantum computing, renowned for its capacity in parallel processing and intricate data analysis, presents a novel avenue to enhance sentiment analysis. This study employs quantum-inspired algorithms to process and examine sentiments within real-time Twitter data, contributing to a more comprehensive comprehension of user opinions and emotions expressed on the platform.