# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 4 Findings from the Literature Review
### Topic Modeling
Tools like MALLET have been employed for LDA implementations, as seen in the work of [PERSON] [78] on abstracts, while libraries such as gensimand sklearn, are frequently used for applying LDA, NMF, and other models. Visual tools, such as pyLDAvis and word clouds, are often utilized to display topic distributions and improve interpretability. Topic modeling has been widely applied in transportation research, showcasing its adaptability to diverse applications. One significant area is public transportation analysis, where topic modeling helps identify operational challenges and user concerns. For instance, [PERSON] and [PERSON] [38] combined LDA with BERT to study public sentiment toward UK public transportation services, highlighting key themes like affordability, safety, and delays. Another crucial application is in real-time monitoring and incident detection. [PERSON] et al. [27] applied topic modeling to tweets from the West Midlands, enhancing traffic management by identifying real-time traffic events and disruptions. [PERSON] and [PERSON] [72] leveraged topic modeling to analyze travel during COVID-19, revealing shifts in public opinion and travel behavior during different phases of the pandemic. These applications demonstrate how topic modeling can enhance decision making for transportation operators by providing actionable insights into dynamic scenarios. Topic modeling has also played a critical role in sustainability and urban mobility initiatives. [PERSON] et al. [20] utilized hybrid methods combining LDA and BiLSTM to assess topics involving low-carbon travel initiatives, uncovering public support for environmentally friendly transportation policies. [PERSON] et al. [31] employed semantic taxonomy and LDA to analyze urban mobility issues on social media, identifying concerns about environmental impacts and inefficiencies in urban transit systems. These insights have informed policies promoting sustainable urban mobility and non-motorized transport. In tourism-related transportation, topic modeling has helped uncover accessibility issues and improve the user experience. For example, [PERSON] et al. [79] applied LDA to reviews of transport systems at Mount Etna, identifying criticalities like limited public transport options and cost concerns. These findings informed recommendations for increasing public transport frequency and accessibility to enhance tourist satisfaction. Findings from topic modeling in transportation research reveal critical insights into user concerns, operational challenges, and emerging trends. One major discovery is its ability to uncover recurring issues, such as affordability, safety, and delays, which often negatively impact user satisfaction. By analyzing patterns in public feedback, researchers have identified key areas where transportation systems can improve service quality and operational efficiency. These insights have informed strategies to address user dissatisfaction and enhance overall service delivery. In the context of sustainability and urban mobility, topic modeling has highlighted public support for environmentally friendly initiatives and exposed inefficiencies in urban transportation systems. It has provided evidence-based recommendations to promote low-carbon travel, reduce environmental impact, and improve accessibility. Findings have also emphasized the importance of addressing infrastructure gaps and inefficiencies to meet sustainability goals while enhancing the user experience. Temporal and contextual dynamics are another significant area of insight. Topic modeling has captured shifts in public sentiment during periods of disruption, such as heightened concerns during crises and improved sentiment following the implementation of safety measures. These findings demonstrate the method's ability to track evolving public attitudes and provide valuable input for adaptive policy-making and service adjustments.