Quantum software engineering is advancing rapidly in parallel with equally ambitious hardware roadmaps. However, systematic evidence on how online audiences perceive these advances remains scarce. We present an exploratory baseline of Twitter sentiment toward quantum computing, using automated (silver‐standard) labels for benchmarking. Six months of English‐language tweets containing the hashtag #Quantum (December 1, 2022 and May 31, 2023) were processed, with #Quantum treated as a proxy for online discourse on quantum computing. We then applied a transparent natural language processing (NLP) methodology combining two zero‐shot lexicon‐based tools (TextBlob and the Valence Aware Dictionary and sEntiment Reasoner [VADER]) with three lightweight supervised classifiers (multinomial naïve Bayes, Rocchio, and perceptron). Following standard preprocessing and a stratified 70/30 train–test split, we do not aim to measure definitive public opinion; rather, our primary contribution is to establish a transparent and reproducible baseline for future benchmarking. In this context, the multinomial naïve Bayes classifier attained a macro F1‐score of 0.88 on the 30% hold‐out set when benchmarked against the TextBlob silver labels. This score captures internal agreement rather than accuracy against human annotation. All five methods converged on a largely—though not universally—positive sentiment orientation (≈78%–81% of nonneutral tweets, depending on the tool). Grounded in the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), we interpret our results as indicating the constructs of curiosity and perceived usefulness, rather than unequivocal adoption readiness. These constructs were not operationalized and serve only as interpretative lenses. By documenting every preprocessing step and model configuration, and making tweet identifiers and code available upon request, the study delivers a reproducible benchmark against which future work can (i) extend the query vocabulary, (ii) incorporate neutral and fine‐grained emotions, (iii) apply cross‐validation protocols, and (iv) evaluate advanced transformer models on manually annotated data. Addressing these four points is essential before making any definitive claims about public opinion.
Quantum software engineering is advancing rapidly in parallel with equally ambitious hardware roadmaps. However, systematic evidence on how online audiences perceive these advances remains scarce. We present an exploratory baseline of Twitter sentiment toward quantum computing, using automated (silver‐standard) labels for benchmarking. Six months of English‐language tweets containing the hashtag #Quantum (December 1, 2022 and May 31, 2023) were processed, with #Quantum treated as a proxy for online discourse on quantum computing. We then applied a transparent natural language processing (NLP) methodology combining two zero‐shot lexicon‐based tools (TextBlob and the Valence Aware Dictionary and sEntiment Reasoner [VADER]) with three lightweight supervised classifiers (multinomial naïve Bayes, Rocchio, and perceptron). Following standard preprocessing and a stratified 70/30 train–test split, we do not aim to measure definitive public opinion; rather, our primary contribution is to establish a transparent and reproducible baseline for future benchmarking. In this context, the multinomial naïve Bayes classifier attained a macro F1‐score of 0.88 on the 30% hold‐out set when benchmarked against the TextBlob silver labels. This score captures internal agreement rather than accuracy against human annotation. All five methods converged on a largely—though not universally—positive sentiment orientation (≈78%–81% of nonneutral tweets, depending on the tool). Grounded in the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), we interpret our results as indicating the constructs of curiosity and perceived usefulness, rather than unequivocal adoption readiness. These constructs were not operationalized and serve only as interpretative lenses. By documenting every preprocessing step and model configuration, and making tweet identifiers and code available upon request, the study delivers a reproducible benchmark against which future work can (i) extend the query vocabulary, (ii) incorporate neutral and fine‐grained emotions, (iii) apply cross‐validation protocols, and (iv) evaluate advanced transformer models on manually annotated data. Addressing these four points is essential before making any definitive claims about public opinion.
Abstract Sentiment analysis is one of the most well-known applications of natural language processing (NLP) techniques used to determine a text's sentiment or emotional tone, such as a sentence, a paragraph, or an entire document. The goal of sentiment analysis is to identify and extract the underlying sentiment expressed by the author, whether positive or negative. Social media platforms like Twitter, Facebook, and Google + are quickly gaining popularity due to the ability for users to share and express their opinions on many subjects, engage in conversation with different organizations, and broadcast messages globally. Sentiment analysis has been extensively studied to track and understand developer comments and views. Quantum software engineering develops software for quantum computers, which use quantum computing to process data. It has gained significant prominence in the field of software technology. Quantum computing may tackle issues that classical computers cannot, advancing cryptography, optimization, and material science. This study aims to explore the social media user review for quantum computing technology innovation in the current era. For this purpose, sentiment analysis applies to social media user reviews for quantum computing technology use. The extracted data is scrubbed through preprocessing techniques. TextBlob, VADER, and supervised learning classification methods have analyzed the sentiments and topics extracted from social media. Results show that quantum users are satisfied with using this soft computing technology and find this experience a successful, positive review for innovative quantum computing technology.
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.
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.
Social media has become a significant resource for evaluating public attitudes regarding mental health, as individuals may express their thoughts through postings, comments, and conversations. Sentiment analysis is a promising method for studying emotional patterns expressed in mental-health-related text. Traditional machine learning for sentiment analysis faces challenges like transparency, scarce labeled data, and language limitations. This paper presents a sentiment classification (positive, negative, neutral) framework applied to mental-health-related Reddit posts. Repeated emotional patterns can offer helpful insights into sentiment trends in mental health discussions, but they shouldn’t be seen as proof of clinical disorders. In this paper, we propose a hybrid framework for sentiment classification from text, termed QBERT-LSTM, which integrates quantum-enhanced bidirectional encoder representations from transformers (QBERT) with long short-term memory (LSTM) networks. In the proposed approach, transformer models excel at capturing global context, while the LSTM enhances sequential patterns and temporal features. The quantum component uses a parameterized circuit with angle encoding and variational gates to perform nonlinear transformations on a quantum simulator (PennyLane). Subsequently, the model is tested on more intricate circuits comprising 2, 4, 6, 8, 10, and 12 qubits to assess its consistency. A novel Reddit dataset of mental health-related subreddits (anxiety, panic attacks, and depression) is constructed, with sentiment labels derived from the VADER sentiment analyzer. We thoroughly assessed the performance of our proposed model against deep learning models and cutting-edge transformer architectures, achieving up to 98% accuracy in the best-performing configuration on the generated dataset (mean accuracy 0.97±0.0059 across five random seeds).
The quantum computing made complex computations may be executed at light speed, superpositions allow for parallelism, and massive quantities of data can be efficiently processed. Furthermore, researchers and engineers are quickly becoming interested in natural language processing (NLP) as a topic for developing more comprehensive computational models of NLP. Therefore, there is hope for future progress in NLP efforts that use quantum technology, especially in emotion classification. In this study, we use quantum computing models to delve deeply into two sentiment categorization approaches. To be more specific, the effectiveness of the models is evaluated by looking at VQC and QSVM. The analyzed models are evaluated by simulations in the Qiskit simulator and the real-time quantum computer Qiskit_ibm_osaka. Results demonstrate the importance of the experiment and validate it using positive predicted value, sensitivity and Fl-Score as performance metrices.
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.