# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 4 Findings from the Literature Review
### Social Media Data  
This section examines the application of social media data in transportation research that utilizes these machine learning models. Figure 8 presents a subset of the selected studies that explicitly reported using social media platforms, such as Twitter, Facebook, and Instagram, as primary data sources. Only studies that provided clear and direct information regarding the platforms employed were included in the final visualization. In contrast, studies that did not specify the use of social media data, as well as general literature reviews summarizing previous works, were excluded from this specific quantitative analysis (Figure 8), as they did not involve direct applications of sentiment analysis or topic modeling. Nevertheless, these articles were not removed from the overall review and were considered in later sections for contextualization and theoretical discussion.  
In total, 62 studies were considered, as they directly reported the application of social media data. These studies detailed the types of platforms used, the nature of the content analyzed (e.g., tweets, posts, reviews), and their specific contributions to the research objectives.  
Twitter dominates as the most frequently used platform, representing approximately 59.5% of the studies. This strong preference can be attributed to Twitter's real-time data availability, extensive user base, and open API access, making it particularly effective for applications such as sentiment analysis and topic modeling.  
In comparison, platforms such as Facebook (11.9%) and Instagram (5.9%) are less commonly used, likely due to data accessibility challenges and stricter privacy policies. Specialized platforms, such as TripAdvisor (4.8%) and Weibo (3.6%), are employed in more niche research contexts, often focused on tourism and region-specific transportation insights. The \"Others\" category represents a diverse group of emerging or less commonly used platforms.  
Another important aspect of social media data is the large volume of information collected across different studies. Figure 9 illustrates the distribution of data sizes used in unique publications related to transportation research, presented on a logarithmic scale and sorted in ascending order. The data sizes vary significantly, ranging from small datasets with fewer than 100 entries to very large ones exceeding one million entries. This variation highlights the diverse scope of studies, with smaller datasets often focusing on detailed or localized analyses, while larger datasets cater to broader and computationally intensive research.  
The figure also emphasizes the prominence of larger datasets, particularly those derived from social media platforms like Twitter, which are frequently used in transportation research for real-time and large-scale analyses. The representation of unique publications ensures that multiple contributions from the same authors or groups are distinctly identified, providing a comprehensive view of the field.  
Figure 8: Use of social media in research (percentage).  
Figure 9: Data size by unique publication.