{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:45:16Z","timestamp":1767422716667,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Internet service providers (ISPs) conduct their business by providing Internet access features to their customers. The COVID-19 pandemic has shifted most activity being performed remotely using an Internet connection. As a result, the demand for Internet services increased by 50%. This significant rise in the appeal of Internet services needs to be overtaken by a notable increase in the service quality provided by ISPs. Service quality plays a great role for enterprises, including ISPs, in retaining consumer loyalty. Thus, modelling ISPs\u2019 service quality is of great importance. Since a common technique to reveal service quality is a timely and costly pencil survey-based method, this work proposes a framework based on the Sentiment Analysis (SA) of the Twitter dataset to model service quality. The SA involves the majority voting of three machine learning algorithms namely Na\u00efve Bayes, Multinomial Na\u00efve Bayes and Bernoulli Na\u00efve Bayes. Making use of Thaicon\u2019s service quality metrics, this work proposes a formula to generate a rating of service quality accordingly. For the case studies, we examined two ISPs in Indonesia, i.e., By.U and MPWR. The framework successfully extracted the service quality rate of both ISPs, revealing that By.U is better in terms of service quality, as indicated by a service quality rate of 0.71. Meanwhile, MPWR outperforms By.U in terms of customer service.<\/jats:p>","DOI":"10.3390\/informatics9010011","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T23:02:06Z","timestamp":1643497326000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Modelling Service Quality of Internet Service Providers during COVID-19: The Customer Perspective Based on Twitter Dataset"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3275-0995","authenticated-orcid":false,"given":"Bagus Setya","family":"Rintyarna","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Universitas Muhammadiyah Jember, Jember 68124, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0300-7286","authenticated-orcid":false,"given":"Heri","family":"Kuswanto","sequence":"additional","affiliation":[{"name":"Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}]},{"given":"Riyanarto","family":"Sarno","sequence":"additional","affiliation":[{"name":"Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}]},{"given":"Emy Kholifah","family":"Rachmaningsih","sequence":"additional","affiliation":[{"name":"Department of Government Studies, Universitas Muhammadiyah Jember, Jember 68124, Indonesia"}]},{"given":"Fika Hastarita","family":"Rachman","sequence":"additional","affiliation":[{"name":"Department of Informatics, Universitas Trunojoyo Madura, Bangkalan 69162, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5781-2008","authenticated-orcid":false,"given":"Wiwik","family":"Suharso","sequence":"additional","affiliation":[{"name":"Department of Informatics, Universitas Muhammadiyah Jember, Jember 68124, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2893-5313","authenticated-orcid":false,"given":"Triawan Adi","family":"Cahyanto","sequence":"additional","affiliation":[{"name":"Department of Informatics, Universitas Muhammadiyah Jember, Jember 68124, Indonesia"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1108\/APJML-03-2015-0039","article-title":"The effects of service quality on internet service provider customers\u2019 behaviour: A mixed methods study","volume":"28","author":"Quach","year":"2016","journal-title":"Asia Pac. J. Mark. Logist."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alamsyah, A., and Bernatapi, E.A. (2019, January 19\u201320). Evolving Customer Experience Management in Internet Service Provider Company using Text Analytics. Proceedings of the 2019 International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia.","DOI":"10.1109\/ICISS48059.2019.8969828"},{"key":"ref_3","first-page":"12","article-title":"SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality","volume":"64","author":"Berry","year":"1988","journal-title":"J. Retail."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100933","DOI":"10.1016\/j.stueduc.2020.100933","article-title":"HEISQUAL: A modern approach to measure service quality in higher education institutions","volume":"67","author":"Abbas","year":"2020","journal-title":"Stud. Educ. Eval."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.jretconser.2014.06.006","article-title":"The development of service quality dimensions for internet service providers: Retaining customers of different usage patterns","volume":"21","author":"Thaichon","year":"2014","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_6","first-page":"1009","article-title":"Mapping acceptance of Indonesian organic food consumption under COVID-19 pandemic using Sentiment Analysis of Twitter dataset","volume":"99","author":"Rintyarna","year":"2021","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_7","unstructured":"Rintyarna, B.S., Sarno, R., and Fatichah, C. (2018). Enhancing the performance of sentiment analysis task on product reviews by handling both local and global context. Int. J. Inf. Decis. Sci., 11."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/s40537-019-0246-8","article-title":"Evaluating the performance of sentence level features and domain sensitive features of product reviews on supervised sentiment analysis tasks","volume":"6","author":"Rintyarna","year":"2020","journal-title":"J. Big Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.dss.2018.06.012","article-title":"Which online reviews do consumers find most helpful? A multi-method investigation","volume":"113","author":"Eslami","year":"2018","journal-title":"Decis. Support Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wallach, H. (2016). Conclusion: Computational social science: Toward a collaborative future. Comput. Soc. Sci. Discov. Predict.","DOI":"10.1017\/CBO9781316257340.014"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101558","DOI":"10.1016\/j.techsoc.2021.101558","article-title":"Using diffusion of innovation theory and sentiment analysis to analyze attitudes toward driving adoption by Saudi women","volume":"65","author":"Alrowily","year":"2021","journal-title":"Technol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rogers, E.M., Singhal, A., and Quinlan, M.M. (2019). Diffusion of Innovations, Taylor and Francis.","DOI":"10.4324\/9780203710753-35"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jacomy, M., Venturini, T., Heymann, S., and Bastian, M. (2014). ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0098679"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hutto, C.J., and Gilbert, E. (2014, January 1\u20134). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, Ann Arbor, MI, USA.","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"ref_15","first-page":"100114","article-title":"Machine Learning with Applications Restaurant recommender system based on sentiment analysis","volume":"6","author":"Asani","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_16","unstructured":"Baccianella, S., Esuli, A., and Sebastiani, F. (2010, January 17\u201323). SentiwordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. Proceedings of the Ninth International Conference on Language Resources and Evaluation, Valletta, Malta."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rintyarna, B.S., Sarno, R., and Fatichah, C. (2019). Semantic Features for Optimizing Supervised Approach of Sentiment Analysis on Product Reviews. MDPI Computers, 8.","DOI":"10.3390\/computers8030055"},{"key":"ref_18","first-page":"100152","article-title":"A comparative analysis of service quality among ECOWAS seaports","volume":"6","author":"Sakyi","year":"2020","journal-title":"Transp. Res. Interdiscip. Perspect."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.sbspro.2011.09.131","article-title":"An investigation of the effects of technology readiness on technology acceptance in e-HRM","volume":"24","author":"Esen","year":"2011","journal-title":"Procedia-Soc. Behav. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101815","DOI":"10.1016\/j.jairtraman.2020.101815","article-title":"The impact of airline service quality on passengers\u2019 behavioral intentions using passenger satisfaction as a mediator","volume":"85","author":"Shah","year":"2020","journal-title":"J. Air Transp. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.eswa.2016.10.065","article-title":"Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN","volume":"72","author":"Chen","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.ijproman.2006.09.003","article-title":"Integrating information technology in the construction industry: Technology readiness assessment of Malaysian contractors","volume":"25","author":"Jaafar","year":"2007","journal-title":"Int. J. Proj. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"53","DOI":"10.5116\/ijme.4dfb.8dfd","article-title":"Making sense of Cronbach\u2019s alpha","volume":"2","author":"Tavakol","year":"2011","journal-title":"Int. J. Med. Educ."},{"key":"ref_24","first-page":"491","article-title":"Soft similarity and soft cosine measure: Similarity of features in vector space model","volume":"18","author":"Sidorov","year":"2014","journal-title":"Comput. Sist."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lewis, D.D. (1998, January 21\u201323). Naive (Bayes) at forty: The independence assumption in information retrieval. Proceedings of the European Conference on Machine Learning, Chemnitz, Germany.","DOI":"10.1007\/BFb0026666"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/1\/11\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:11:02Z","timestamp":1760134262000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/1\/11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,29]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["informatics9010011"],"URL":"https:\/\/doi.org\/10.3390\/informatics9010011","relation":{},"ISSN":["2227-9709"],"issn-type":[{"type":"electronic","value":"2227-9709"}],"subject":[],"published":{"date-parts":[[2022,1,29]]}}}