# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 1 Introduction  
In an increasingly connected world, transportation systems are crucial to urban development, economic growth, and environmental responsibility. However, they also face pressing challenges related to operational efficiency, user satisfaction, and the persistent failure to return to pre-pandemic levels of public transportation usage [1]. Increasing congestion, inadequate infrastructure, and poor integration of transportation modes affect urban mobility and quality of life in cities.  
On the other hand, the advent of social media has provided unprecedented access to real-time user-generated data, reflecting public perceptions, behavioral patterns and emerging societal trends--an unprecedented technological advancement for urban analysis [2]. This wealth of information has catalyzed innovative applications of machine learning in the transportation domain, enabling a deeper understanding of user satisfaction, operational inefficiencies, and sustainability challenges.  
Sentiment analysis enables the measurement of public opinion, highlighting areas for service improvement and informing policy decisions [3], while topic modeling uncovers latent themes and trends that shape the broader narrative of urban mobility [4]. These approaches are particularly effective in leveraging data from platforms such as Twitter, Facebook, and Instagram, offering a dynamic lens through which to view the evolving needs of transportation systems.  
Despite their potential, these techniques face challenges. Linguistic diversity, regional variations, and the complexity of integrating multimodal datasets pose significant obstacles to scalability and generalizability. Moreover, short-text formats, such as tweets, demand advanced models to extract meaningful insights effectively [5]. Addressing these issues is crucial to fully realizing the benefits of social media analytics in transportation research.  
In this context, the present review synthesizes recent advancements in sentiment analysis and topic modeling, examining their applications and limitations in the transportation domain. By examining diverse studies, the work aims to illuminate pathways for integrating these methodologies into smarter, more sustainable mobility solutions. Through a comprehensive exploration of methodologies, applications, and challenges, this study contributes to advancing the field, fostering data-driven innovations in transportation systems worldwide. Additionally, this review seeks to facilitate progress in studies on mobility patterns in urban areas influenced by major trip-generating hubs, such as universities, enabling comparisons with mobility dynamics in the broader context of the city in which they are located.  
The main contributions of this review are (i) to provide a domain-specific methodological framework for identifying and analyzing relevant studies at the intersection of sentiment analysis, topic modeling, and transportation, using bibliometric and network tools adapted to this context; (ii) to synthesize comparative insights regarding platforms, data sizes, and model performance related to the application of machine learning models to opinion classification and topic detection; and (iii) to critically discuss current gaps, limitations, and ethical concerns in the field.  
The remainder of this paper is structured as follows. Section 2 reviews related work in sentiment analysis and topic modeling within transportation. Section 3 describes the review methodology. Section 4 presents the main findings and analytical results. Section 5 offers a critical discussion. Section 6 concludes the paper with final remarks and future directions.