# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 4 Findings from the Literature Review
### Topic Modeling  
In the context of natural language processing and machine learning, topic modeling is an automatic technique that attempts to extract the most important topics within a collection of text documents [70]. By analyzing patterns of word co-occurrence, it clusters words and phrases into groups that represent coherent subjects, allowing researchers to uncover hidden structures in large datasets without prior knowledge of the content. Topic modeling is widely applied in fields such as information retrieval, content recommendation, sentiment analysis, and academic research, offering a scalable way to organize, summarize, and explore unstructured textual data efficiently. Topic modeling has also been extensively used in transportation research to uncover latent themes and trends within large datasets. Various methods have been applied, with Latent Dirichlet Allocation (LDA) being one of the most popular approaches. For instance, [PERSON] and [PERSON] [8] used LDA to analyze 17,163 transportation research abstracts, identifying emerging trends, such as sustainability and non-motorized mobility. Similarly, [PERSON] et al. [30] applied LDA to public transport-related tweets during the COVID-19 pandemic in London, revealing topics like safety measures and ridership changes. [PERSON] et al. [71] utilized LDA to analyze hotline complaints in China, identifying issues such as fare disputes and unlicensed taxis. Despite its popularity, LDA's limitations in handling short texts, such as tweets, have prompted the adoption of advanced models, such as Non-Negative Matrix Factorization (NMF), BERTopic, Top2 Vec, and Structural Topic Modeling (STM), which deliver superior granularity and contextual relevance. For example, [PERSON] and [PERSON] [72] compared NMF with other topic modeling techniques for analyzing Twitter posts and found it particularly effective for generating clear and novel insights. BERTopic and Top2 Vec have proven useful for short and unstructured text, such as social media posts. The authors also demonstrated BERTopic's ability to extract nuanced and interpretable themes, while Top2 Vec provided broader topic categorizations. Structural Topic Modeling (STM) is another advanced approach, particularly useful for integrating metadata into the analysis. [PERSON] [73] applied STM to aviation safety reports, incorporating contextual information, such as flight phases, to identify latent trends. Similarly, [PERSON] et al. [74] used STM to analyze COVID-19-related transportation research articles, uncovering themes such as travel behavior changes and logistics optimization. However, some works have successfully applied Latent Dirichlet Allocation (LDA) topic modeling to tweets. For example, [PERSON] and [PERSON] [75] analyzed tweets from Traffic Management Centers (TMCs) to model topics related to road traffic. The key topics identified included regular traffic monitoring, traffic rule appeals, announcements, and traffic accident reports. [PERSON] and [PERSON] [76] used LDA alongside tools such as Senpy and RapidMiner to analyze tweets from Uber's customer support (@Uber_Support) during 2020. The identified topics included app-related issues and driver concerns. Similarly, [PERSON] et al. [77] analyzed tweets and newspaper articles to study public opinion on driverless cars. The topics covered included safety, congestion, labor market impacts, and legal liability. In another study, [PERSON] and [PERSON] [28] employed the same model to analyze tweets related to public transport performance in Sydney. Twelve major topics were identified, including delays, cancellations, service updates, and the effects of government policies. Hybrid approaches have also gained traction, combining topic modeling with other techniques for enhanced insights. [PERSON] et al. [42] integrated Latent Dirichlet Allocation with fuzzy ontology and word embeddings to analyze sentiment and trends in transportation datasets.