Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
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...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6576 |
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| author | Ewerton Chaves Moreira Torres Luís Guilherme de Picado-Santos |
| author_facet | Ewerton Chaves Moreira Torres Luís Guilherme de Picado-Santos |
| author_sort | Ewerton Chaves Moreira Torres |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-38ba0dcdc4ce47f3ad3b5115f39eab85 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-38ba0dcdc4ce47f3ad3b5115f39eab852025-08-20T03:26:20ZengMDPI AGApplied Sciences2076-34172025-06-011512657610.3390/app15126576Sentiment Analysis and Topic Modeling in Transportation: A Literature ReviewEwerton Chaves Moreira Torres0Luís Guilherme de Picado-Santos1CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, PortugalCERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, PortugalThe 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.https://www.mdpi.com/2076-3417/15/12/6576social media analyticssentiment analysistopic modelingsustainable transportationmachine learningtransportation research |
| spellingShingle | Ewerton Chaves Moreira Torres Luís Guilherme de Picado-Santos Sentiment Analysis and Topic Modeling in Transportation: A Literature Review Applied Sciences social media analytics sentiment analysis topic modeling sustainable transportation machine learning transportation research |
| title | Sentiment Analysis and Topic Modeling in Transportation: A Literature Review |
| title_full | Sentiment Analysis and Topic Modeling in Transportation: A Literature Review |
| title_fullStr | Sentiment Analysis and Topic Modeling in Transportation: A Literature Review |
| title_full_unstemmed | Sentiment Analysis and Topic Modeling in Transportation: A Literature Review |
| title_short | Sentiment Analysis and Topic Modeling in Transportation: A Literature Review |
| title_sort | sentiment analysis and topic modeling in transportation a literature review |
| topic | social media analytics sentiment analysis topic modeling sustainable transportation machine learning transportation research |
| url | https://www.mdpi.com/2076-3417/15/12/6576 |
| work_keys_str_mv | AT ewertonchavesmoreiratorres sentimentanalysisandtopicmodelingintransportationaliteraturereview AT luisguilhermedepicadosantos sentimentanalysisandtopicmodelingintransportationaliteraturereview |