# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 4 Findings from the Literature Review
### Topic Modeling
To conclude, the growing complexity of transportation datasets has driven the adoption of advanced methods, such as BERTopic, STM, and hybrid models, which complement the foundational role of LDA. These approaches have proven essential for analyzing social media data, integrating metadata, and uncovering actionable insights. Topic modeling has consistently uncovered critical themes, including public sentiment, operational inefficiencies, and opportunities for sustainability, guiding targeted interventions and policy decisions. Overall, by revealing latent patterns and adapting to diverse contexts, topic modeling remains a powerful tool for advancing transportation research. Its ability to inform service improvements, promote low-carbon mobility, and address evolving challenges underscores its ongoing relevance in fostering smarter, more sustainable transportation systems worldwide. Other relevant works have presented different approaches related to the use of sentiment analysis and topic modeling with application in the transportation domain. Table 2 shows a summary of these studies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref./Year** & **Method** & **Application** & **Findings** \\ \hline \multirow{6}{*}{[73]} & Structural Topic Modeling (STM) applied to & Identifying themes and trends in aviation & Demonstrated STM’s utility for integrating metadata and \\  &  database), using metadata (flight mission, & safety to inform safety priorities and & narrative text for aviation safety insights. \\  & phase of flight, etc.) to uncover latent topics & future research. & \\  & and trends. & & \\ \hline \multirow{6}{*}{[80]} & Latent Dirichlet Allocation (LDA) applied to & Assessing road safety on Irish roads, focusing & Common roadside hazards include poles, roadside \\  & Road Safety Inspection (ISI) reports to & on run-off-road crashes and identifying & Issues are more frequently identified than solutions, \\  & identify patterns in roadside safety issues and & common problems and corresponding & indicating better intervention strategies are needed. \\  & interventions. & interventions. & Lack of application of “clear zone” and “forgiving \\  & & & \\ \hline \multirow{6}{*}{[81]} & Twitter primarily captures negative sentiment (\(5\%\)) & compared to surveys, which report more balanced & feedback. \\  & Sentiment analysis using manual classification & Measuring user satisfaction with the & Thematic topics from tweets aligned well with operational \\  & and topic modeling with MALLET, validation & Tansantago public transport system in & issues yet revealed biases towards rush hours and higher \\  & of results via comparative analysis with & sentiago, Chile, using Twitter data to & socioeconomic areas. \\  & traditional surveys. & complement traditional surveys. & Twitter data provides broader spatial coverage than \\  & & & \\ \hline \multirow{6}{*}{[82]} & Structural Topic Model (STM) applied to & Assessing the quality of service in the & Temporal analysis showed a rise in experience-related topics and a decline in physical operations-related topics. \\  & 52,087 online reviews from Damping, com; & Shanghai Metro system by identifying key & Spatial clustering revealed differing priorities across \\  & & themes, analyzing sentiment polarity, and & station types: residential areas emphasized commuting \\  & stopword removal, and part-of-speech tagging. & needs, business districts highlighted design and \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scientific studies using sentiment analysis and topic modeling in the transport domain.