{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T23:07:52Z","timestamp":1777417672267,"version":"3.51.4"},"reference-count":51,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":344,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/00013\/2025"],"award-info":[{"award-number":["UID\/00013\/2025"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Software"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>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\u2010standard) labels for benchmarking. Six months of English\u2010language 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\u2010shot lexicon\u2010based tools (TextBlob and the Valence Aware Dictionary and sEntiment Reasoner [VADER]) with three lightweight supervised classifiers (multinomial na\u00efve Bayes, Rocchio, and perceptron). Following standard preprocessing and a stratified 70\/30 train\u2013test 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 na\u00efve Bayes classifier attained a macro F1\u2010score of 0.88 on the 30% hold\u2010out 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\u2014though not universally\u2014positive sentiment orientation (\u224878%\u201381% 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\u2010grained emotions, (iii) apply cross\u2010validation 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.<\/jats:p>","DOI":"10.1049\/sfw2\/1648095","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T19:46:35Z","timestamp":1765482395000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sentiment Analysis of Twitter Data on Quantum Computing: An Exploratory Silver\u2010Label Baseline Study"],"prefix":"10.1049","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5275-3292","authenticated-orcid":false,"given":"Faisal","family":"Mehmood","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0870-1285","authenticated-orcid":false,"given":"Abeer Abdulaziz","family":"Alsanad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6880-4991","authenticated-orcid":false,"given":"Muhammad Azeem","family":"Akbar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4755-3270","authenticated-orcid":false,"given":"V\u00edctor","family":"Leiva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9897-8186","authenticated-orcid":false,"given":"Cecilia","family":"Castro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-6522-2_1"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12599-023-00823-w"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-021-03021-3"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3548679"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-05324-5"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111326"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/spe.3039"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1103\/PRXQuantum.2.017001"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sasc.2022.200031"},{"key":"e_1_2_10_10_2","doi-asserted-by":"crossref","unstructured":"KorenkovV. V. ReshetnikovA. G. andUlyanovS. V. Quantum Software Engineering Supremacy in Intelligent Robotics Proceedings of the International Scientific and Technical Conference Modern Computer Network Technologies 2020 Moscow Russia IEEE 1\u20136 https:\/\/doi.org\/10.1109\/MoNeTeC49726.2020.9258000.","DOI":"10.1109\/MoNeTeC49726.2020.9258000"},{"key":"e_1_2_10_11_2","unstructured":"ZhaoJ. Quantum Software Engineering: Landscapes and Horizons 2020 arXiv preprint arXiv: 2007.07047https:\/\/doi.org\/10.48550\/arXiv.2007.07047."},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2021.3099140"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.2307\/249008"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.2307\/30036540"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-023-01030-x"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2436256.2436274"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55394-8"},{"key":"e_1_2_10_18_2","doi-asserted-by":"crossref","unstructured":"NeshanS. A. S.andAkbariR. A Combination of Machine Learning and Lexicon Based Techniques for Sentiment Analysis Proceedings of the 6th International Conference on Web Research 2020 Tehran Iran IEEE 8\u201314 https:\/\/doi.org\/10.1109\/ICWR49608.2020.9122298.","DOI":"10.1109\/ICWR49608.2020.9122298"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1049\/2024\/5550801"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109780"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.2556"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.10.076"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21238020"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/sym13040682"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.3390\/math11143069"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.3390\/math10040554"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116143"},{"key":"e_1_2_10_28_2","unstructured":"BelalM. SheJ. andWongS. Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis 2023 arXiv preprint arXiv: 2306.17177https:\/\/doi.org\/10.48550\/arXiv.2306.17177."},{"key":"e_1_2_10_29_2","first-page":"1097","article-title":"Sentiment Analysis: Textblob for Decision Making","volume":"7","author":"Gujjar J. P.","year":"2021","journal-title":"International Journal of Advance Scientific Research and Engineering Trends"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1049\/2024\/8846233"},{"key":"e_1_2_10_31_2","doi-asserted-by":"crossref","unstructured":"AssiriA. GumaeiA. MehmoodF. andUllahS. Social Media User Evaluation for Quantum Computing Technology via Sentiment Analysis Research Square 2024 https:\/\/doi.org\/10.21203\/rs.3.rs-3999636\/v1.","DOI":"10.21203\/rs.3.rs-3999636\/v1"},{"key":"e_1_2_10_32_2","doi-asserted-by":"crossref","unstructured":"LiuB. HuM. andChengJ. Opinion Observer: Analyzing and Comparing Opinions on the Web Proceedings of the 14th International Conference on World Wide Web 2005 Association for Computing Machinery 342\u2013351 https:\/\/doi.org\/10.1145\/1060745.1060797.","DOI":"10.1145\/1060745.1060797"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03070-3_13"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-018-1236-4"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10442-2"},{"key":"e_1_2_10_36_2","unstructured":"PakA.andParoubekP. Twitter as a Corpus for Sentiment Analysis and Opinion Mining Proceedings of the Seventh International Conference on Language Resources and Evaluation 2010 European Language Resources Association (ELRA) 1320\u20131326."},{"key":"e_1_2_10_37_2","unstructured":"GoA. BhayaniR. andHuangL. Twitter Sentiment Classification Using Distant Supervision 2009 Stanford University Stanford CA US Technical Report CS224N."},{"key":"e_1_2_10_38_2","doi-asserted-by":"crossref","unstructured":"BifetA.andFrankE. Sentiment Knowledge Discovery in Twitter Streaming Data Proceeding of the International Conference on Discovery Science 2010 New York NY Springer US 1\u201315.","DOI":"10.1007\/978-3-642-16184-1_1"},{"key":"e_1_2_10_39_2","unstructured":"AgarwalA. XieB. VovshaI. RambowO. andPassonneauR. J. Sentiment Analysis of Twitter Data Proceedings of the Workshop on Language in Social Media 2011 Association for Computational Linguistics 30\u201338."},{"key":"e_1_2_10_40_2","doi-asserted-by":"crossref","unstructured":"LiangP. W.andDaiB. R. Opinion Mining on Social Media Data 2 Proceeding of the 14th International Conference on Mobile Data Management 2013 Milan Italy IEEE 91\u201396.","DOI":"10.1109\/MDM.2013.73"},{"key":"e_1_2_10_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.11.023"},{"key":"e_1_2_10_42_2","doi-asserted-by":"crossref","unstructured":"OthmanR. AbdelsadekY. ChelghoumK. KacemI. andFaizR. Improving Sentiment Analysis in Twitter Using Sentiment Specific Word Embeddings 2 Proceedings of the 10th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications 2019 Metz France IEEE 854\u2013858.","DOI":"10.1109\/IDAACS.2019.8924403"},{"key":"e_1_2_10_43_2","unstructured":"BarbieriF. Camacho-ColladosJ. Espinosa-AnkeL. andNevesL. XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond 2021 arXiv preprint arXiv: 2104.12250."},{"key":"e_1_2_10_44_2","doi-asserted-by":"crossref","unstructured":"ConneauA. KhandelwalK. GoyalN. ChaudharyV. andWenzekG. et al.Unsupervised Cross-Lingual Representation Learning at Scale Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 2020 Association for Computational Linguistics 8440\u20138451.","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"e_1_2_10_45_2","unstructured":"HeP. GaoJ. andChenW. DeBERTa-V3: Improving DeBERTa Using ELECTRA-Style Pre-Training With Gradient Disentanglement 2023 arXiv preprint arXiv: 2301.00001."},{"key":"e_1_2_10_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-025-01434-x"},{"key":"e_1_2_10_47_2","unstructured":"HonnibalM. MontaniI. Van LandeghemS. andBoydA. spaCy: Industrial-Strength Natural Language Processing in Python (Version 3.0) 2020 Zenodo https:\/\/doi.org\/10.5281\/zenodo.1212303."},{"key":"e_1_2_10_48_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"e_1_2_10_49_2","doi-asserted-by":"publisher","DOI":"10.47738\/jads.v2i1.17"},{"key":"e_1_2_10_50_2","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2500"},{"key":"e_1_2_10_51_2","doi-asserted-by":"publisher","DOI":"10.1080\/17576180.2025.2452774"}],"container-title":["IET Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/sfw2\/1648095","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/sfw2\/1648095","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/sfw2\/1648095","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:12:09Z","timestamp":1773029529000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/sfw2\/1648095"}},"subtitle":[],"editor":[{"given":"Deepsubhra","family":"Guha Roy","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/sfw2\/1648095"],"URL":"https:\/\/doi.org\/10.1049\/sfw2\/1648095","archive":["Portico"],"relation":{},"ISSN":["1751-8806","1751-8814"],"issn-type":[{"value":"1751-8806","type":"print"},{"value":"1751-8814","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-04-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-06","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"1648095"}}