# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 3 Review Methodology
### Bibliographic Analysis  
VOSviewer, a tool for bibliometric and network analysis, was employed to focus the search on the specific topic of the research and to visually map the network of key concepts within the selected scientific articles and their connections. An initial graph  
Figure 2: Publications over time. Source: OpenAlex.  
created with VOSviewer after the first search revealed clusters containing concepts related to the study's subject.  
The graph is displayed in Figures 3-6 in three different formats to enhance interpretation. Each figure emphasizes a key term relevant to the study's topic and its connections to other terms: Figure 2 highlights \"transport\", Figure 3 \"twitter\", Figure 4 \"topic\" and Figure 5 \"classification\".  
The bibliographic analysis performed using VOSviewer offers valuable insights into the interconnected themes and research priorities within the transportation field. The network visualization reveals three primary clusters, representing distinct yet interconnected domains: sentiment analysis (green cluster), topic modeling and trends (red cluster), and platform-based studies, such as those involving Twitter (blue cluster).  
The red cluster is dominated by keywords such as \"topic modeling\", \"Latent Dirichlet Allocation\", and \"trend\", which emphasize the methodological focus of transportation research. It highlights the widespread application of topic modeling techniques to uncover latent trends in public sentiment, operational inefficiencies, and sustainability-related issues. In this sense, LDA continues to be a cornerstone of topic modeling for transportation datasets, offering significant insights into themes such as safety, government policies, and public opinion.  
Figure 5: Co-occurrences of “topic”. Source: VOSviewer.  
Figure 6: Co-occurrences of “classification”. Source: VOSviewer.  
The green cluster focuses on performance, accuracy, and machine learning models, such as BERT, which are central to sentiment analysis research. This cluster underscores the evolution of sentiment analysis techniques, particularly the adoption of deep learning models, which have significantly improved accuracy and contextual understanding. It illustrates how these methods are being used to classify user feedback, identify operational gaps, and predict behavioral trends.  
The blue cluster, centered around social media platforms, reflects the critical role of data sources, such as Twitter, in real-time transportation research. Keywords such as \"feedback\", \"platform\", and \"comment\" indicate the use of user-generated content to monitor public sentiment and improve service delivery. This cluster also highlights the integration of geospatial data and metadata to enhance the contextual relevance of insights.  
Together, these clusters reveal the multifaceted nature of transportation research, where advanced methodologies like topic modeling and sentiment analysis intersect with real-world applications. The bibliographic analysis demonstrates the growing importance of leveraging diverse data sources, addressing challenges such as linguistic variations, and integrating multimodal datasets to improve transportation systems globally. These findings provide a roadmap for future research, emphasizing the need for innovative, scalable, and adaptive analytical frameworks.