# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 4 Findings from the Literature Review
### Sentiment Analysis
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref./Year** & **Method** & **Application** & **Findings** \\ \hline
[49] & Sentiment analysis using Support Vector Machines (SVM) and delta TF-IDF; event detection based on keyword matching. & Improving urban mobility in Milan by analyzing Twitter data for detecting transport events and assessing user sentiment toward public transportation services. & SVM proved effective for sentiment classification with high accuracy. \\ \hline
[50] & Optimal Transport (OT) loss applied to sentiment analysis for minimizing misclassifications between opposite sentiment classes; compared against traditional cross-entropy loss. & Analyzing user sentiment toward U.S. airlines using the Kaggle Twitter US Airline Sentiment dataset. & OT loss improved misclassification rates between positive and negative sentiment classes compared to cross-entropy. Overall accuracy was slightly improved, demonstrating OT’s potential for enhancing sentiment classification tasks. Results emphasize OT’s utility for handling imbalanced classes in real-world applications. \\ \hline
[51] & Sentiment analysis using Support Vector Machine (SVM) with unigram and TF-IDF for feature extraction; preprocessing included text cleaning, stemming, and tokenization. & Analyzing public sentiment about the online transportation service Golek, categorizing tweets as positive or negative to assess customer satisfaction. & Cotives sentiment was identified with low-precision positive sentiment showed 67.44% precision. \\ \hline
[52] & Sentiment analysis using an English opinion lexicon, unigram-based clustering for topic analysis; test preprocessing included emoj translation and data cleaning. & Understanding public opinions about Los Angeles’ light rail system to enhance service delivery and policy-making. & Temporal analysis revealed weekly patterns, such as more positive tweets on Mondays and negative ones during weekends. \\ \hline
[53] & Corpus-based analysis using Indonesian Web Corpus (IWAC) and hava news collection from Nouses in Indonesia to identify underlying social issues, such as distrust in government and unawareness of transportation laws. & Highlighted Twitter’s potential as a tool for real-time feedback on transit services. \\ \hline \end{tabular}
\end{table} 
Table 1: Cont. \begin{table}
\begin{tabular}{c c c c} \hline
**Ref./Year** & **Method** & **Application** & **Findings** \\ \hline
[54] & \begin{tabular}{c} Complaint classification using logistic \\ regression and neural models (MLP and \\ LSTM); dataset created via manual annotation \\ of Twitter complaints across nine domains, \\ including transport. \\ \end{tabular}  & \ \begin{tabular}{c} Identifying and analyzing complaints on \\ social media to enhance customer service and \\ support dialogue systems. \\ \end{tabular}  & \begin{tabular}{c} Transportation combined layer performance due to \\ linguistic variations in complaint expression. \\ Proposed models significantly outperformed baseline \\ sentiment analysis tools in compliant classification. \\ \end{tabular}  \\ \hline
[55] & \begin{tabular}{c} Content analysis of Yelp reviews using a \\ coding scheme developed from literature and \\ refined via manual review; analyzed factors \\ affecting user evaluations and complaints. \\ \end{tabular}  & \ \begin{tabular}{c} Investigating perceptions of bike-sharing \\ systems in New York, Washington, D.C., and \\ Chicago, focusing on regional and temporal \\ variations in user satisfaction. \\ \end{tabular}  & \begin{tabular}{c} Average ratings were consistent across regions \\ (around 2.6/5). \\ Key factors influencing satisfaction included pricing, bike \\ quality, and customer service. \\ Highlighted the role of social media in detecting service \\ issues and planning improvements. \\ \end{tabular}  \\ \hline
[56] &