# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 6 Conclusions  
This review highlights the transformative potential of sentiment analysis and topic modeling in the transportation domain. These methods have proven invaluable for understanding public sentiment, identifying operational challenges, and fostering sustainable practices. By leveraging social media data, researchers have gained actionable insights into user experiences and systemic inefficiencies, enabling targeted interventions and policy reforms. The findings emphasize the role of advanced machine learning and deep learning techniques in capturing complex and nuanced data. These methods have consistently delivered high accuracy and contextual relevance, supporting the development of smarter and more sustainable transportation systems. Furthermore, the integration of topic modeling with sentiment analysis has offered a more comprehensive understanding of transportation dynamics, effectively bridging gaps between operational insights and strategic decision making. Despite these contributions, this review has certain limitations. First, it focused exclusively on English-language, open-access publications, which may have excluded valuable insights from non-English or subscription-only sources. Second, while the selection and analysis processes were designed to be systematic and transparent, it is important to acknowledge that systematic reviews--by their very nature--may overlook relevant studies due to strict inclusion criteria or database limitations. Consequently, some innovative or context-specific contributions may not have been captured. Future research should prioritize addressing existing challenges, such as enhancing model scalability, incorporating multilingual capabilities, and integrating multimodal data. Expanding the application of these methods to underserved regions and contexts is also essential for achieving a more equitable and comprehensive understanding of global transportation issues. Additionally, improving methodological transparency and evaluation is crucial. This includes the systematic use of reproducible metrics, such as the F1-score for sentiment classification and probabilistic coherence for topic modeling, as well as the application of stability tests and cross-validation procedures. The integration of SA and TM into joint modeling frameworks remains underexplored and warrants further investigation. In conclusion, the integration of sentiment analysis and topic modeling represents a significant step forward in transportation research. By uncovering latent patterns and adapting to diverse contexts, these methodologies are poised to play an increasingly central role in optimizing transportation systems and policies. Their ongoing evolution and application will undoubtedly contribute to the advancement of sustainable, efficient, and user-focused mobility solutions worldwide. Conceptualization, E.C.M.T. and L.G.d.P.-S.; methodology, E.C.M.T. and L.G.d.P.-S.; software, E.C.M.T. ; validation, E.C.M.T. ; formal analysis, E.C.M.T. and L.G.d.P.-S.; investigation, E.C.M.T. ; resources, E.C.M.T. and L.G.d.P.-S.; data curation, E.C.M.T. ; writing--original draft preparation, E.C.M.T. ; writing--review and editing, E.C.M.T. and L.G.d.P.-S.; visualization, E.C.M.T. ; supervision, L.G.d.P.-S. All authors have read and agreed to the published version of the manuscript. The authors acknowledge the financial support of the Foundation for Science and Technology (FCT) through the project UIDB/04625/2025 of the research unit CERIS (DOI: 10.54499/UIDB/04625/2020). The authors declare no conflict of interest.