{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T02:52:06Z","timestamp":1776739926301,"version":"3.51.2"},"reference-count":94,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foundation for Science and Technology","award":["UIDB\/04625\/2025"],"award-info":[{"award-number":["UIDB\/04625\/2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The growing use of social media data has opened new avenues for understanding user perceptions and operational inefficiencies in transportation systems. Among the most widely adopted analytical approaches for extracting insights from these data are sentiment analysis and topic modeling, which enable researchers to capture public opinion trends and uncover latent themes in unstructured content. However, despite a rising number of individual studies, systematic reviews focusing specifically on these approaches in transportation research remain limited, particularly in addressing methodological challenges and data heterogeneity. This literature review addresses that gap by critically examining 81 open-access studies published between 2014 and 2024. The main challenges identified include handling linguistic diversity, integrating multimodal and geolocated data, managing short-text formats, and addressing regional and demographic bias. In response, this review proposes a methodological framework for study selection and bibliometric analysis, classifies the most commonly applied machine learning models for sentiment and topic extraction, and synthesizes findings regarding data sources, model performance, and application contexts in transportation. Additionally, it discusses unresolved gaps and ethical concerns related to representativeness and social media governance. This review highlights the transformative potential of combining sentiment analysis and topic modeling to support smarter, more inclusive, and sustainable transportation policies by offering an integrative and critical perspective.<\/jats:p>","DOI":"10.3390\/app15126576","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T11:18:40Z","timestamp":1749640720000},"page":"6576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Sentiment Analysis and Topic Modeling in Transportation: A Literature Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0188-9588","authenticated-orcid":false,"given":"Ewerton Chaves Moreira","family":"Torres","sequence":"first","affiliation":[{"name":"CERIS, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2072-3188","authenticated-orcid":false,"given":"Lu\u00eds Guilherme","family":"de Picado-Santos","sequence":"additional","affiliation":[{"name":"CERIS, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ziedan, A., Brakewood, C., and Watkins, K. (2023). Will Transit Recover? A Retrospective Study of Nationwide Ridership in the United States during the COVID-19 Pandemic. J. Public Transp., 25.","DOI":"10.1016\/j.jpubtr.2023.100046"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.compenvurbsys.2018.11.001","article-title":"Social Media Data: Challenges, Opportunities and Limitations in Urban Studies","volume":"74","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_3","unstructured":"Wang, S., Zhao, Z., Xie, Y., Ma, M., Chen, Z., Wang, Z., Su, B., Xu, W., and Li, T. (2024). Recent Surge in Public Interest in Transportation: Sentiment Analysis of Baidu Apollo Go Using Weibo Data. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Avetisyan, L., Zhang, C., Bai, S., Pari, E.M., Feng, F., Bao, S., and Zhou, F. (2022). Design a Sustainable Micro-Mobility Future: Trends and Challenges in the United States and European Union Using Natural Language Processing Techniques. arXiv.","DOI":"10.1080\/09544828.2022.2142904"},{"key":"ref_5","unstructured":"Wang, X., Jiang, W., and Luo, Z. (2016, January 11\u201316). Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts. Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6383","DOI":"10.1007\/s11192-021-04046-2","article-title":"Investigating Transportation Research Based on Social Media Analysis: A Systematic Mapping Review","volume":"126","author":"Zayet","year":"2021","journal-title":"Scientometrics"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Verma, S. (2022). Sentiment Analysis of Public Services for Smart Society: Literature Review and Future Research Directions. Gov. Inf. Q., 39.","DOI":"10.1016\/j.giq.2022.101708"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.trc.2017.01.013","article-title":"Discovering Themes and Trends in Transportation Research Using Topic Modeling","volume":"77","author":"Sun","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_9","unstructured":"Kherwa, P., and Bansal, P. (2020). Topic Modeling: A Comprehensive Review. EAI Endorsed Trans. Scalable Inf. Syst., 7."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tonkin, E.L. (2016). A Day at Work (with Text): A Brief Introduction. Working with Text: Tools, Techniques and Approaches for Text Mining, Elsevier.","DOI":"10.1016\/B978-1-84334-749-1.00002-0"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"373","DOI":"10.2495\/UT140311","article-title":"Analyzing Tweets to Enable Sustainable, Multi-Modal and Personalized Urban Mobility: Approaches and Results from the Italian Project TAM-TAM","volume":"Volume 138","author":"Candelieri","year":"2014","journal-title":"WIT Transactions on the Built Environment"},{"key":"ref_12","first-page":"57","article-title":"Sentiment Analysis on Twitter about the Use of City Public Transportation Using Support Vector Machine Method","volume":"2","author":"Effendy","year":"2016","journal-title":"Int. J. Inf. Commun. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sari, E.Y., Wierfi, A.D., and Setyanto, A. (2019, January 3\u20135). Sentiment Analysis of Customer Satisfaction on Transportation Network Company Using Naive Bayes Classifier. Proceedings of the International Conference on Computer Engineering Network, and Intelligent Multimedia, London, UK.","DOI":"10.1109\/CENIM48368.2019.8973262"},{"key":"ref_14","first-page":"335","article-title":"Sentiment Analysis on Online Transportation Services Using Convolutional Neural Network Method","volume":"Volume 1","author":"Ashari","year":"2021","journal-title":"Proceedings of the International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Anastasia, S., and Budi, I. (2016, January 15\u201316). Twitter Sentiment Analysis of Online Transportation Service Providers. Proceedings of the 2016 International Conference on Advanced Computer Science and Information Systems, Malang, Indonesia.","DOI":"10.1109\/ICACSIS.2016.7872807"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pratama, M.O., Satyawan, W., Jannati, R., Pamungkas, B., Syahputra, M.E., and Neforawati, I. (2019, January 18). The Sentiment Analysis of Indonesia Commuter Line Using Machine Learning Based on Twitter Data. Proceedings of the Journal of Physics: Conference Series, Crete, Greece.","DOI":"10.1088\/1742-6596\/1193\/1\/012029"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lopez-Fuentes, L., Farasin, A., Zaffaroni, M., Skinnemoen, H., and Garza, P. (2020). Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data. Appl. Sci., 10.","DOI":"10.3390\/app10248783"},{"key":"ref_18","first-page":"239","article-title":"Sentiment Analysis of Tweets on Transport from \u00cele-de-France","volume":"2","author":"Jacques","year":"2018","journal-title":"ACL Anthol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"389","DOI":"10.25046\/aj050549","article-title":"Sentiment Analysis on Utilizing Online Transportation of Indonesian Customers Using Tweets in the Normal Era and the Pandemic COVID-19 Era with Support Vector Machine","volume":"5","author":"Jaman","year":"2020","journal-title":"Adv. Sci. Technol. Eng. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, X.M., Ho, C.H., Xia, L.T., and Zhao, R.Y. (2021). Sentiment Analysis of Low-Carbon Travel APP User Comments Based on Deep Learning. Sustain. Energy Technol. Assess., 44.","DOI":"10.1016\/j.seta.2021.101014"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hirata, E., and Matsuda, T. (2023). Examining Logistics Developments in Post-Pandemic Japan through Sentiment Analysis of Twitter Data. Asian Transp. Stud., 9.","DOI":"10.1016\/j.eastsj.2023.100110"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, Z., and Di, X. (2023). Sentiment Analysis on Multimodal Transportation during the COVID-19 Using Social Media Data. Information, 14.","DOI":"10.3390\/info14020113"},{"key":"ref_23","first-page":"92","article-title":"Sentiment Analysis of Users\u2019 Perception Towards Public Transportation Using TWITTER","volume":"2","author":"Fen","year":"2020","journal-title":"Int. J. Technol. Manag. Inf. Syst."},{"key":"ref_24","first-page":"210","article-title":"Twitter to Transport: Geo-Spatial Sentiment Analysis of Traffic Tweets to Discover People\u2019s Feelings for Urban Transportation Issues","volume":"13","author":"Chaturvedi","year":"2019","journal-title":"J. East. Asia Soc. Transp. Stud."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Beck, D., Teixeira, M., Mar\u00f3stica, J., and Ferasso, M. (2024). Quality Perception of S\u00e3o Paulo Transportation Services: A Sentiment Analysis of Citizens\u2019 Satisfaction Regarding Bus Terminuses. Rev. Gest. Ambient. Sustentabilidade, 13.","DOI":"10.5585\/2024.23392"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s12469-021-00289-7","article-title":"Decoding Customer Experiences in Rail Transport Service: Application of Hybrid Sentiment Analysis","volume":"15","author":"Mishra","year":"2023","journal-title":"Public Transp."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Salas, A., Georgakis, P., Nwagboso, C., Ammari, A., and Petalas, I. (2017, January 23\u201326). Traffic Event Detection Framework Using Social Media. Proceedings of the IEEE International Conference on Smart Grid and Smart Cities (ICSGSC), Singapore.","DOI":"10.1109\/ICSGSC.2017.8038595"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1080\/10095020.2020.1815596","article-title":"Social Media as Passive Geo-Participation in Transportation Planning\u2013How Effective Are Topic Modeling & Sentiment Analysis in Comparison with Citizen Surveys?","volume":"23","author":"Lock","year":"2020","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.iatssr.2022.03.003","article-title":"Road Traffic Conditions in Kenya: Exploring the Policies and Traffic Cultures from Unstructured User-Generated Data Using NLP","volume":"46","author":"Muguro","year":"2022","journal-title":"IATSS Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Politis, I., Georgiadis, G., Kopsacheilis, A., Nikolaidou, A., and Papaioannou, P. (2021). Capturing Twitter Negativity Pre-vs. Mid-COVID-19 Pandemic: An Lda Application on London Public Transport System. Sustainability, 13.","DOI":"10.3390\/su132313356"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trpro.2017.05.059","article-title":"Sustainability Analysis on Urban Mobility Based on Social Media Content","volume":"Volume 24","author":"Serna","year":"2017","journal-title":"Transportation Research Procedia"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Serna, A., Soroa, A., and Agerri, R. (2021). Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport. Sustainability, 13.","DOI":"10.3390\/su13042397"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vitetta, A. (2022). Sentiment Analysis Models with Bayesian Approach: A Bike Preference Application in Metropolitan Cities. J. Adv. Transp., 2022.","DOI":"10.1155\/2022\/2499282"},{"key":"ref_34","unstructured":"Baj-Rogowska, A. (2018, January 13\u201315). Sentiment Analysis of Facebook Posts: The Uber Case. Proceedings of the 8th IEEE International Conference on Intelligent Computing and Information Systems, Madurai, India."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Garzia, F., Borghini, F., Moretti, A., Lombardi, M., and Ramalingam, S. (2021, January 11\u201315). Emotional Analysis of Safeness and Risk Perception of Transports and Travels by Car and Motorcycle in London and Rome during the COVID-19 Pandemic. Proceedings of the International Carnahan Conference on Security Technology, Hatfield, UK.","DOI":"10.1109\/ICCST49569.2021.9717374"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.35940\/ijeat.A2636.109119","article-title":"Sentiment Evaluation of Public Transport in Social Media Using Na\u00efve Bayes Method","volume":"9","author":"Othman","year":"2019","journal-title":"Int. J. Eng. Adv. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Saragih, M.H., and Girsang, A.S. (2017, January 24\u201325). Sentiment Analysis of Customer Engagement on Social Media in Transport Online. Proceedings of the International Conference on Sustainable Information Engineering and Technology, Batu, Indonesia.","DOI":"10.1109\/SIET.2017.8304103"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Aksan, A., and Akda\u01e7, H.C. (2023, January 5\u20138). Public Opinion on UK Public Transportation Through Sentiment Analysis and Topic Modeling. Proceedings of the 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Istanbul, Turkey.","DOI":"10.1109\/SIU59756.2023.10223775"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1177\/23998083221104489","article-title":"Monitoring the Well-Being of Vulnerable Transit Riders Using Machine Learning Based Sentiment Analysis and Social Media: Lessons from COVID-19","volume":"50","author":"Tran","year":"2023","journal-title":"Environ. Plan B Urban Anal. City Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"10434","DOI":"10.1016\/j.jksuci.2022.10.031","article-title":"Towards Solving NLP Tasks with Optimal Transport Loss","volume":"34","author":"Bhardwaj","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Giancristofaro, G.T., and Panangadan, A. (2016, January 1\u20134). Predicting Sentiment toward Transportation in Social Media Using Visual and Textual Features. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795898"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.trc.2017.01.014","article-title":"Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling","volume":"77","author":"Ali","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_43","unstructured":"Myoya, R.L. (2024). Analysing Public Transport User Sentiment. [Master\u2019s Thesis, University of Pretoria]."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"117","DOI":"10.33003\/fjs-2023-0706-2057","article-title":"Enhancing user experience through sentiment analysis for katsina state transport agency: A textblob approach","volume":"7","author":"Ajik","year":"2023","journal-title":"Fudma J. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sari, I.C., and Ruldeviyani, Y. (2020, January 17). Sentiment Analysis of the COVID-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers. Proceedings of the 2020 International Workshop on Big Data and Information Security, IWBIS 2020, Depok, Indonesia.","DOI":"10.1109\/IWBIS50925.2020.9255531"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TITS.2013.2291241","article-title":"Web-Based Traffic Sentiment Analysis: Methods and Applications","volume":"15","author":"Cao","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"29","DOI":"10.30935\/mjosbr\/12790","article-title":"SentiSfaction: New Cultural Way to Measure Tourist COVID-19 Mobility in Italy","volume":"7","author":"Papapicco","year":"2023","journal-title":"Mediterr. J. Soc. Behav. Res."},{"key":"ref_48","first-page":"576","article-title":"Traffic Issues Categorization of Indian Cities Using Word2Vec by Social Media Data","volume":"7","author":"Trivedi","year":"2020","journal-title":"J. Emerg. Technol. Innov. Res."},{"key":"ref_49","unstructured":"Candelieri, A., and Archetti, F. (2015, January 4\u20138). Detecting Events and Sentiment on Twitter for Improving Urban Mobility. Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Istanbul, Turkey."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lazi\u0107, J., Krsti\u0107, A., and Vujnovi\u0107, S. (2023, January 5\u20138). Sentiment Analysis Using Optimal Transport Loss Function. Proceedings of the 10th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2023, East Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/IcETRAN59631.2023.10192163"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pertiwi Windasari, I., Nurul Uzzi, F., and Iman Satoto, K. (2017, January 18\u201319). Sentiment Analysis on Twitter Posts: An Analysis of Positive or Negative Opinion on GoJek. Proceedings of the 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering, Semarang, Indonesia.","DOI":"10.1109\/ICITACEE.2017.8257715"},{"key":"ref_52","unstructured":"Luong, T.T.B., and Houston, D. (2015, January 24\u201327). Public Opinions of Light Rail Service in Los Angeles, an Analysis Using Twitter Data. Proceedings of the iConference 2015 Proceedings, Newport Beach, CA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Pinem, Y.A. (2021). Corpus-Based Analysis of Online Hoax Discourse on Transportation Subject Picturing Indonesian Issue. Ling. Cult., 15.","DOI":"10.21512\/lc.v15i1.7067"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Preotiuc-Pietro, D., Gaman, M., and Aletras, N. (2019, January 2\u20137). Automatically Identifying Complaints in Social Media. Proceedings of the Association for Computational Linguistics, Minneapolis, MN, USA.","DOI":"10.18653\/v1\/P19-1495"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1080\/15568318.2020.1830320","article-title":"A Comparative Study of Bike-Sharing Systems from a User\u2019s Perspective: An Analysis of Online Reviews in Three U.S. Regions between 2010 and 2018","volume":"15","author":"Shin","year":"2021","journal-title":"Int. J. Sustain. Transp."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1111\/jors.12524","article-title":"Public Transport, Noise Complaints, and Housing: Evidence from Sentiment Analysis in Singapore","volume":"61","author":"Fan","year":"2021","journal-title":"J. Reg. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.trpro.2020.10.074","article-title":"Traffic Safety Evaluation in Northwestern Federal District Using Sentiment Analysis of Internet Users\u2019 Reviews","volume":"50","author":"Seliverstov","year":"2020","journal-title":"Transp. Res. Procedia"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Adilah, M.T., Supendar, H., Ningsih, R., Muryani, S., and Solecha, K. (2020). Sentiment Analysis of Online Transportation Service Using the Na\u00efve Bayes Methods. J. Phys. Conf. Ser., 1641.","DOI":"10.1088\/1742-6596\/1641\/1\/012093"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Gao, S., Ran, Q., Su, Z., Wang, L., Ma, W., and Hao, R. (2024). Evaluation System for Urban Traffic Intelligence Based on Travel Experiences: A Sentiment Analysis Approach. Transp. Res. Part A Policy Pract., 187.","DOI":"10.1016\/j.tra.2024.104170"},{"key":"ref_60","unstructured":"Nurkholis, A., Aldino, A.A., Samsugi, S., Suryati, E., and Cahyono, R.P. (2022, January 29\u201330). Sentiment Analysis on Online Transportation Reviews Using Word2Vec Text Embedding Model Feature Extraction and Support Vector Machine (SVM) Algorithm. Proceedings of the 2021 International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2021, Jakarta, Indonesia."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Atmadja, A.R., Uriawan, W., Pritisen, F., Maylawati, D.S., and Arbain, A. (2019). Comparison of Naive Bayes and K-Nearest Neighbours for Online Transportation Using Sentiment Analysis in Social Media. J. Phys. Conf. Ser., 1402.","DOI":"10.1088\/1742-6596\/1402\/7\/077029"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1109\/TFUZZ.2020.2970834","article-title":"Sentiment Analysis for Driver Selection in Fuzzy Capacitated Vehicle Routing Problem with Simultaneous Pick-Up and Drop in Shared Transportation","volume":"29","author":"Gupta","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_63","first-page":"6407","article-title":"Sentiment Analysis to Improve the Quality of Public Services \u201cSuroboyo Bus\u201d","volume":"7","author":"Kumalasari","year":"2024","journal-title":"Indones. Interdiscip. J. Sharia Econ."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Bakalos, N., Papadakis, N., and Litke, A. (2020). Public Perception of Autonomous Mobility Using ML-Based Sentiment Analysis over Social Media Data. Logistics, 4.","DOI":"10.3390\/logistics4020012"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Rohwinasakti, S., Irawan, B., and Setianingsih, C. (2021, January 11\u201313). Sentiment Analysis on Online Transportation Service Products Using K-Nearest Neighbor Method. Proceedings of the International Conference on Computer Information, and Telecommunication Systems CITS 2021, Istanbul, Turkey.","DOI":"10.1109\/CITS52676.2021.9618301"},{"key":"ref_66","first-page":"132","article-title":"Improving Airport Services Using Sentiment Analysis of the Websites","volume":"22","author":"Gitto","year":"2017","journal-title":"Tour. Manag. Perspect."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jairtraman.2019.01.004","article-title":"Social Media as a Resource for Sentiment Analysis of Airport Service Quality (ASQ)","volume":"78","author":"Mandsberg","year":"2019","journal-title":"J. Air Transp. Manag."},{"key":"ref_68","first-page":"466","article-title":"Sentiment Analysis of Public Transportation Services on Twitter Social Media Using the Method Na\u00efve Bayes Classifier","volume":"5","author":"Aldisa","year":"2021","journal-title":"Int. J. Inf. Syst. Technol. Akreditasi"},{"key":"ref_69","first-page":"153","article-title":"Public Sentiment Analysis of Online Transportation in Indonesia through Social Media Using Google Machine Learning","volume":"9","year":"2021","journal-title":"J. Ilm. Merpati"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wagner, S., and Fern\u00e1ndez, D.M. (2015). Analyzing Text in Software Projects. The Art and Science of Analyzing Software Data, Elsevier Inc.","DOI":"10.1016\/B978-0-12-411519-4.00003-3"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wu, R., Shao, C., Zhuge, C., Wang, X., and Yin, X. (2024, November 24). What Do People Complain about Transport Service? Text Mining of Hotline Data Using LDA Model 2022. Available online: https:\/\/ssrn.com\/abstract=4305469.","DOI":"10.2139\/ssrn.4305469"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Egger, R., and Yu, J. (2022). A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Front. Sociol., 7.","DOI":"10.3389\/fsoc.2022.886498"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.trc.2017.12.018","article-title":"Using Structural Topic Modeling to Identify Latent Topics and Trends in Aviation Incident Reports","volume":"87","author":"Kuhn","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tranpol.2023.06.001","article-title":"Discovering Latent Topics and Trends in Autonomous Vehicle-Related Research: A Structural Topic Modelling Approach","volume":"139","author":"Tamakloe","year":"2023","journal-title":"Transp. Policy"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Hidayatullah, A.F., and Ma\u2019arif, M.R. (2017, January 24\u201325). Road Traffic Topic Modeling on Twitter Using Latent Dirichlet Allocation. Proceedings of the 2017 International Conference on Sustainable Information Engineering and Technology (SIET), Malang, Indonesia.","DOI":"10.1109\/SIET.2017.8304107"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Moreno, A., and Iglesias, C.A. (2021). Understanding Customers\u2019 Transport Services with Topic Clustering and Sentiment Analysis. Appl. Sci., 11.","DOI":"10.3390\/app112110169"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.tranpol.2020.05.026","article-title":"Examining the Potential of Textual Big Data Analytics for Public Policy Decision-Making: A Case Study with Driverless Cars in Denmark","volume":"98","author":"Kinra","year":"2020","journal-title":"Transp. Policy"},{"key":"ref_78","first-page":"266","article-title":"Horizon 2020 Project Analysis by Using Topic Modelling Techniques in the Field of Transport","volume":"25","year":"2024","journal-title":"Transp. Telecommun."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.trpro.2023.02.188","article-title":"A Sentiment Analysis Approach to Investigate Tourist Satisfaction towards Transport Systems: The Case of Mount Etna","volume":"69","author":"Fazio","year":"2023","journal-title":"Transp. Res. Procedia"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.aap.2019.07.021","article-title":"Topic Analysis of Road Safety Inspections Using Latent Dirichlet Allocation: A Case Study of Roadside Safety in Irish Main Roads","volume":"131","author":"Roque","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"60255","DOI":"10.1109\/ACCESS.2019.2915107","article-title":"Using Twitter to Infer User Satisfaction with Public Transport: The Case of Santiago, Chile","volume":"7","author":"Mendez","year":"2019","journal-title":"IEEE Access"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.jum.2024.07.008","article-title":"How Do People Perceive the Quality of Urban Transport Service? New Insights from Online Reviews of Shanghai Metro System","volume":"13","author":"Dou","year":"2024","journal-title":"J. Urban Manag."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Ye, Q., Chen, X., Zhang, H., Ozbay, K., and Zuo, F. (2019, January 1). Public Concerns and Response Pattern toward Shared Mobility Using Social Media Data. Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917010"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1080\/12265934.2022.2044891","article-title":"Discovering Research Topics, Trends, and Perspectives in COVID-19-Related Transportation Journal Articles","volume":"26","author":"Tamakloe","year":"2022","journal-title":"Int. J. Urban Sci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1080\/15472450.2022.2026773","article-title":"What Do Riders Say and Where? The Detection and Analysis of Eyewitness Transit Tweets","volume":"27","author":"Kabbani","year":"2023","journal-title":"J. Intell. Transp. Syst. Technol. Plan. Oper."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Aksan, A., and Akda\u011f, H.C. (2024). Comparative Analysis of Public Transportation Through Sentiment Analysis and Topic Modeling. Industrial Engineering in the Industry 4.0 Era, Springer Science and Business Media Deutschland GmbH.","DOI":"10.1007\/978-3-031-53991-6_1"},{"key":"ref_87","unstructured":"Ali, F., El-Sappagh, S., Ali, A., Kwak, K.S., Ei-Sappagh, S., Kwak, K.S., and Kwak, D. (2018, January 25). Sentiment Analysis of Transportation Using Word Embedding and LDA Approaches. Proceedings of the Korea Institute of Communications and Information Sciences 2018 Winter General Academic Conference, Incheon, Republic of Korea."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"\u00c7aylak, P.\u00c7., Kayaku\u015f, M., Eksili, N., Yi\u011fit A\u00e7ikg\u00f6z, F., Co\u015fkun, A.E., Ichimov, M.A.M., and Moiceanu, G. (2024). Analysing Online Reviews Consumers\u2019 Experiences of Mobile Travel Applications with Sentiment Analysis and Topic Modelling: The Example of Booking and Expedia. Appl. Sci., 14.","DOI":"10.3390\/app142411800"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"\u00d6zkara, Y., Bili\u015fli, Y., Yildirim, F.S., Kayan, F., Ba\u015fde\u011firmen, A., Kayaku\u015f, M., and Yi\u011fit A\u00e7\u0131kg\u00f6z, F. (2025). Analysing Social Media Discourse on Electric Vehicles with Machine Learning. Appl. Sci., 15.","DOI":"10.3390\/app15084395"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Deng, J., and Liu, Y. (2025). Research on Sentiment Analysis of Online Public Opinion Based on RoBERTa\u2013BiLSTM\u2013Attention Model. Appl. Sci., 15.","DOI":"10.3390\/app15042148"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Melhem, W.Y., Abdi, A., and Meziane, F. (2024). Deep Learning Classification of Traffic-Related Tweets: An Advanced Framework Using Deep Learning for Contextual Understanding and Traffic-Related Short Text Classification. Appl. Sci., 14.","DOI":"10.3390\/app142311009"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Laynes-Fiascunari, V., Gutierrez-Franco, E., Rabelo, L., Sarmiento, A.T., and Lee, G. (2023). A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning Techniques. Appl. Sci., 13.","DOI":"10.3390\/app13105888"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.knosys.2019.02.033","article-title":"Transportation Sentiment Analysis Using Word Embedding and Ontology-Based Topic Modeling","volume":"174","author":"Ali","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1080\/19427867.2023.2212998","article-title":"Impact of COVID-19 Outbreak and Vaccination on Ride-Sharing Services: A Social Media Analysis","volume":"16","author":"Shokoohyar","year":"2024","journal-title":"Transp. Lett."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/15\/12\/6576\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:50:10Z","timestamp":1760032210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/15\/12\/6576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,11]]},"references-count":94,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["app15126576"],"URL":"https:\/\/doi.org\/10.3390\/app15126576","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,11]]}}}