# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 4 Findings from the Literature Review
### Sentiment Analysis
Conversely, positive sentiment spikes were noted following the implementation of safety measures or vaccination campaigns, highlighting the importance of adaptive and responsive policies. To illustrate, [PERSON] and [PERSON] [38] explored sentiment trends in the UK, noting that responsive policies addressing affordability and accessibility led to spikes in positive sentiment. An important insight is the value of sentiment analysis in uncovering latent trends and informing sustainable practices. By analyzing public sentiment toward low-carbon travel and urban mobility initiatives, studies have provided evidence-based recommendations to enhance accessibility, reduce environmental impact, and improve overall user experience. Findings also demonstrate the potential of sentiment analysis to address broader societal concerns, such as equity in transit access and perceptions of safety. Specifically, [PERSON] et al. [39] assessed the behavioral and psychological responses of vulnerable transit riders in Metro Vancouver, highlighting inequities in transit access and safety concerns, particularly for low-income groups and women. Despite these successes, some findings reveal limitations and areas for improvement. Challenges include addressing linguistic and regional variations, ensuring the scalability of models across diverse contexts, and handling imbalanced datasets. For instance, [PERSON] et al. [40] applied an Optimal Transport (OT) loss framework to improve sentiment classification in imbalanced datasets, demonstrating enhanced accuracy and performance compared to traditional methods. These challenges often result in reduced classification performance or limited generalizability of insights. Additionally, certain studies highlight gaps in the integration of metadata and multimodal data, which could provide a more comprehensive understanding of user sentiment. Therefore, the increasing complexity of datasets calls for adopting advanced machine learning and deep learning techniques to capture nuanced public sentiments. In this sense, [PERSON] and [PERSON] [41] used combined visual (image) and textual data for sentiment analysis of Instagram posts related to transportation services, enhancing classification accuracy. Equally important, [PERSON] et al. [42] integrated metadata and text data using fuzzy ontology for sentiment analysis in transportation, demonstrating improved accuracy and insights. Future research should prioritize addressing challenges such as multilingual datasets, imbalanced data, and multimodal inputs, ensuring that sentiment analysis continues to enhance the efficiency and sustainability of transportation systems globally. [PERSON] et al. [43] investigated sentiment toward public transport, focusing on multilingual and code-mixed datasets, highlighting the need for advanced natural language processing (NLP) tools. In conclusion, the findings underscore the transformative potential of sentiment analysis in the transportation domain. They highlight its utility not only in diagnosing existing problems but also in predicting future trends and shaping proactive strategies. As methodologies advance and datasets become more comprehensive, the insights derived from sentiment analysis are likely to play an increasingly pivotal role in optimizing transportation systems and policies. Finally, other relevant works have presented different approaches related to the use of sentiment analysis with application in the transportation domain. Table 1 shows a summary of these studies.