# 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
[44] & Sentiment analysis using the TextBlob library; preprocessing and polarity scoring of comments from survey data. & Improving customer satisfaction for Katsina & 71\% positive sentiments; 9\% negative, highlighting service areas needing improvement. Recommendations for operational and service enhancements based on feedback. \\ \hline
[45] & Sentiment analysis using Naïve Bayes and Decision Tree algorithms on Twitter data; preprocessing included data cleaning, case folding, s stemming, and tokenizing. & Analyzing public sentiment toward COVID-19 transmission risks among commuter line passengers in Indonesia. & Naive Bayes outperformed Decision Tree with an average accuracy of 73.59\%. \\ \hline
[2020] & Sentiment analysis using Naïve Bayes and Decision Tree algorithms on Twitter data; preprocessing included data cleaning, case folding, s stemming, and tokenizing. & Passing sentiment was the most frequent (135 tweets), followed by negative (152 tweets) and neutral (53 tweets). Sentiment predominantly reflected appeals and calls for COVID-19 prevention and control. \\ \hline
[6] & Systematic mapping review of 74 transportation-related studies using social media data published between 2008 and 2018 & Identifying trends, approaches, data types, and platforms in transportation research leveraging social media analysis; providing insights for future research. & Twitter is the most frequently used platform (72\% of studies), of studies. \\ \hline
[46] & Rule-based sentiment analysis framework for processing traffic-related web data; data isuses (e.g., “yellow light rule” and fuel prices) to support intelligent transportation systems construction. & Analyzing public sentiment on traffic-related studies (e.g., “yellow light rule” and fuel prices) to support intelligent transportation systems (ITS) and policy evaluation. & Rule-based approach achieved higher accuracy for traffic-related sentiment analysis compared to existing algorithms. \\ \hline
[47] & Sentiment analysis using SentiStrength, combined with Chi-square and \(t\)-tests, applied to Twitter data. & Measuring customer satisfaction and online reputation of Trentidia rail services during the pre-COVID-19 (2019) and COVID-19 (2020) periods. & Prevalence of neutral sentiment (41\% in 2019; 64\% in 2020). Mixed emotions highlighted in tweets reflect the impact of the pandemic on mobility and service expectations. Differences in sentiment attributed to contextual factors, emphasizing the need for qualitative analysis alongside quantitative methods. \\ \hline \multicolumn{4}{c}{WordVee classification algorithm and semantic analysis for analyzing traffic-related tweets; preprocessing included data cleaning, hashtag and URL removal, and multilingual tweet conversion.} & Categorizing traffic-related issues (e.g., accidents, congestion, potholes) in Indian cities for improved traffic management. & Tweets categorized into positive, negative, and neutral sentiments. \\ \hline \end{tabular}
\end{table} 
Table 1: Scientific studies using sentiment analysis in the transport domain.