# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 3 Review Methodology
### Selected Studies  
After the analysis of the VOSviewer graph, the search was refined to focus exclusively on open-access publications. Ultimately, 81 studies were selected for detailed exploration (Appendix A). Of the 81 articles selected, 61 (75.3%) focused on sentiment analysis, while only 20 (24.7%) addressed topic modeling. However, it is important to note that some studies combined both methods, meaning the total number of articles using each technique is not mutually exclusive. This discrepancy may indicate a potential bias in the literature, reflecting a greater academic interest and applicability of data for sentiment analysis in transportation research. Conversely, topic modeling, despite its potential, may face methodological challenges in analyzing short-text formats and interpreting generated topics. Additionally, the smaller number of studies may suggest a need for further research exploring this approach to better understand trends and patterns in transportation-related discussions. This imbalance in the literature highlights the importance of future research that integrates both techniques, enabling a more comprehensive and in-depth understanding of public perceptions and dynamics in transportation systems. The geographical distribution of the studies, illustrated in Figure 7, reveals a global focus, with contributions from various countries across multiple continents. Europe dominates the research landscape, with countries such as Italy, the UK, Germany, and Spain frequently represented. Asia also plays a significant role, with contributions from China, India, South Korea, and Japan. These countries reflect a growing focus on using advanced methodologies to address urban mobility challenges in densely populated regions. The high number of studies in China underscores its emphasis on integrating technology with urban transport systems. Figure 7: Study locations. In North America, the United States leads with studies from several states, showcasing its diverse research focus on topics ranging from public sentiment to ride-sharing platforms. South America, represented by countries such as Brazil and Chile, demonstrates a growing interest in addressing regional transportation issues and improving user satisfaction. Africa and Oceania have a smaller but notable presence. Research in Kenya, Nigeria, and South Africa highlights emerging interest in leveraging data-driven methods for transportation in developing regions. Australia also contributes with studies focusing on innovative approaches to public transport. Given this, the application of sentiment analysis and topic modeling in transportation research faces significant challenges shaped by geographical constraints, government policies, and disparities in social media usage. In regions with strict privacy regulations, such as the European Union or Brazil, the collection of geolocated or personal data is limited, restricting real-time analyses or the integration of critical metadata for contextual studies. For example, research relying on Twitter in Europe must work with anonymized data. Additionally, access to social media APIs has become more restricted and costly, particularly after Twitter's policy changes in 2023, disproportionately affecting researchers in lower-resource countries. The choice of platforms also reflects regional inequalities. While Twitter dominates in countries such as the U.S., the UK, and Indonesia, Facebook and Instagram are more relevant in South America, and local platforms have gained prominence in China, where global platforms are blocked. However, methodological bias is also introduced due to linguistic and cultural differences. In Indonesia, for instance, the abundance of Twitter data masks challenges such as spam and informal language, while in regions such as Sub-Saharan Africa, data scarcity limits analytical robustness. This fragmentation also creates representation bias, as urban, younger, and digitally active users are overrepresented, marginalizing rural populations or those with limited internet access.