# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 5 Discussion  
The integration of sentiment analysis and topic modeling into transportation research has offered transformative insights into public perceptions, operational challenges, and areas for improvement. By analyzing vast datasets from social media and other sources, these methods have illuminated critical aspects of transportation systems, such as user satisfaction, safety concerns, and sustainability efforts. Advancements in sentiment analysis have demonstrated their capability to uncover hidden patterns and themes within large and complex transportation datasets. By leveraging hybrid approaches, such as BERTopic and word embeddings, sentiment analysis has improved accuracy and interpretability, allowing researchers to identify specific operational inefficiencies and user concerns. These methods have proven especially useful in addressing user feedback related to urban mobility and sustainability, where they have highlighted barriers like insufficient infrastructure and inefficiencies in public transit systems. Topic modeling techniques, such as Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM), have been particularly effective in identifying latent trends and contextual themes. Despite its limitations in handling short texts, such as tweets, LDA remains a widely used method due to its simplicity and effectiveness in extracting topics from social media data. This application has proven especially valuable in understanding public sentiment and identifying issues in real-time transportation contexts. Quantitatively, only a minority of studies provided standard performance metrics for either sentiment classification or topic modeling. For sentiment analysis, the F1-score and accuracy were the most frequently reported metrics; however, many papers lacked detailed confusion matrices or class distribution analyses. In topic modeling, coherence measures--probabilistic coherence, for example--were inconsistently used across studies. This omission weakens the reproducibility and comparison of models from different datasets and use cases. Probabilistic coherence, in particular, offers a robust way to evaluate topic quality by measuring statistical dependence among top terms within each topic. It outperforms traditional measures, such as perplexity, when interpretability is a priority. However, several studies failed to report it, limiting the validity of their findings. Beyond evaluation gaps, the integration of sentiment analysis (SA) and topic modeling (TM) remains underutilized in transportation research. However, recent studies in related domains offer methodological contributions that highlight the potential of integrated approaches. [PERSON] et al. [88] and [PERSON] et al. [89] demonstrate how SA and TM can be combined to analyze user reviews and multilingual social media content, respectively, revealing how emotional tone aligns with thematic concerns. [PERSON] and [PERSON] [90] propose a RoBERTa-BiLSTM-Attention model that improves sentiment classification across public opinion stages and could inform more structured combinations with topic models. [PERSON] et al. [91] develop a deep learning framework combining BiLSTM, CNN, and multi-level attention to classify traffic-related short texts--an architecture that could be extended to sentiment-aware topic modeling. While not focused directly on SA or TM, [PERSON] et al. [92] emphasize the importance of real-time textual analysis in intelligent transport systems (ITS), reinforcing the value of integrating textual and behavioral analytics. The analysis of temporal dynamics has added depth to transportation research, revealing how public sentiment shifts during disruptive events. This ability to track and respond to evolving public sentiment underscores the potential of these methods to inform adaptive decision making in transportation planning. However, several challenges persist. Linguistic and regional variations remain significant obstacles, alongside the handling of imbalanced datasets.