# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 4 Findings from the Literature Review
### Sentiment Analysis
[25] used Bing Lexicon for sentiment analysis of comments from Google Maps about Sao Paulo's bus terminuses, identifying cleanliness and accessibility as positive aspects. Hybrid models that integrate lexicon-based methods with machine learning techniques enhance classification accuracy by leveraging semantic nuances. These approaches have proven effective in studies evaluating urban mobility and transportation satisfaction, providing deeper insights into user feedback. To illustrate, [PERSON] and [PERSON] [26] combined lexicon-based methods and [PERSON] for sentiment analysis of Indian Railways using Twitter data, enhancing sentiment classification accuracy. Sentiment analysis has become a versatile tool in transportation research, addressing a wide range of applications that enhance both operational efficiency and strategic planning. These include real-time monitoring of urban mobility, evaluating public transit services, managing emergency responses during disasters, and analyzing public sentiment during major disruptions, such as the COVID-19 pandemic. Additionally, sentiment analysis has proven invaluable for brand reputation management and policy assessment, helping transportation providers and policymakers align services with user expectations. One of the key applications of sentiment analysis is in real-time monitoring and incident management. Studies have leveraged social media data to detect traffic events, service disruptions, and emergency conditions. For instance, [PERSON] et al. [27] combined geolocation and sentiment analysis to identify real-time traffic incidents in the West Midlands, significantly improving traffic management. Similarly, [PERSON] and [PERSON] [28] assessed public transport performance in Sydney by using Twitter as a passive geo-participation tool, integrating sentiment analysis with transport network data. In contexts such as natural disasters, [PERSON] et al. [29] analyzed social media data in Kenya to uncover insights into reckless driving and accidents, aiding emergency responses. The model has also been widely used to evaluate transportation services, providing actionable insights into user satisfaction. [PERSON] et al. [12] analyzed user sentiment toward Jakarta's public transportation, revealing critical issues such as long wait times and unsafe driving practices. [PERSON] et al. [30] explored public sentiment about London's transportation system during the pandemic, noting increased negative sentiment during periods of stricter restrictions. Studies focusing on platforms like Twitter, Instagram, and TripAdvisor have identified strengths and weaknesses in commuter lines, online transportation apps, and ride-hailing services. Common findings include areas for improvement, such as service delays, safety concerns, and app usability, enabling providers to refine their services to better meet customer needs. In the context of sustainability and urban mobility, sentiment analysis has been utilized to explore public attitudes toward initiatives such as low-carbon travel and non-motorized transport. For example, [PERSON] et al. [31] used hybrid sentiment analysis methods to identify sustainability-related issues in urban mobility based on social media data from Minube, emphasizing environmental concerns and transportation efficiency. In another study, [PERSON] et al. [32] also evaluated sustainable transportation modes using user-generated content from TripAdvisor. In addition, [PERSON] [33] applied sentiment analysis to social media data to understand public preferences for biking in metropolitan areas, promoting the adoption of non-motorized transport modes. By addressing these aspects, sentiment analysis not only supports environmental goals but also improves the overall quality of urban mobility systems. Sentiment analysis has proven equally valuable for understanding public perception during major disruptions or policy changes. During the COVID-19 pandemic, studies such as those by [PERSON] and [PERSON] [21] and [PERSON] et al.