# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 4 Findings from the Literature Review
### Sentiment Analysis
[22] used sentiment analysis to examine logistics trends in Japan and travel behaviors in New York City, respectively. These insights revealed shifting public opinions on safety measures and service satisfaction, providing valuable guidance for policymakers to implement targeted interventions. Another critical application lies in brand reputation management. [PERSON] [34] conducted a sentiment analysis of Uber's Facebook feedback, highlighting public opinion fluctuations caused by events such as the #DeleteUber campaign. Additionally, Uber customer sentiment studies have emphasized critical areas for improvement, including app functionality and safety measures, showcasing the importance of sentiment analysis in maintaining a competitive edge in the transportation market. Sentiment analysis has also explored emotional responses to individual motorized modes of transport. [PERSON] et al. [35] analyzed reactions to car and motorcycle travel during the pandemic in London and Rome, emphasizing safety concerns. This highlights the utility of sentiment analysis in assessing user preferences and addressing emotional factors that influence transportation choices. Overall, the applications of sentiment analysis in transportation research illustrate its ability to bridge operational insights with strategic decision making. By leveraging advanced models and hybrid methodologies, researchers and practitioners can address complex challenges and foster the development of smarter, more sustainable transportation systems. The growing integration of sentiment analysis into transportation systems underscores its transformative potential for improving safety, accessibility, and user satisfaction across various domains. The findings across the reviewed studies demonstrate the significant potential of sentiment analysis to extract actionable insights and improve various aspects of transportation systems. These findings also highlight both the strengths and limitations of the methods employed, as well as the practical implications for transportation services and policy. A recurring theme is the ability of sentiment analysis to accurately classify sentiments and predict user behavior. Studies using advanced models, such as CNNs, BERT, and BiLSTM, consistently report high accuracy, precision, and recall metrics, reflecting their robustness in handling large datasets and nuanced text [18; 19; 20; 21]. For instance, the integration of word embeddings with machine learning models has yielded sentiment classification accuracies as high as 94% in online transportation reviews. Similarly, topic modeling combined with sentiment analysis has successfully identified key issues, such as service delays and safety concerns, providing clear areas for intervention. Another notable finding is the identification of critical user concerns and operational challenges. Sentiment analysis has been effective in identifying specific issues, such as crowdedness, poor app functionality, and delays, which negatively impact user satisfaction [12; 13; 25]. Studies focusing on public transportation systems and ride-hailing services have uncovered patterns of dissatisfaction, such as unsafe driving, insufficient infrastructure, and inadequate customer service, guiding service providers toward targeted improvements. For example, [PERSON] et al. [36] analyzed social media feedback on Rapid KL transportation in Malaysia, identifying issues with driver attitudes, technical problems, and poor customer service, and [PERSON] and [PERSON] [37] investigated complaints on Twitter and Facebook about ride-hailing services in Indonesia, uncovering dissatisfaction with unsafe driving and lack of responsiveness in customer service. The contributions also emphasize the contextual and temporal shifts in sentiment, particularly during disruptive events like the COVID-19 pandemic. For example, several studies observed increased negative sentiment during periods of stricter lockdown measures, reflecting heightened user stress and dissatisfaction.