# Sentiment Analysis and Topic Modeling in Transportation: A Literature Review
## 4 Findings from the Literature Review
### Sentiment Analysis  
This section summarizes key insights from selected articles on applying sentiment analysis in transportation. The reviewed articles employ diverse sentiment analysis methodologies tailored to transportation research. Machine learning approaches, such as Support Vector Machines (SVM) and Naive Bayes (NB), are widely used for their robustness and simplicity in text classification tasks. Sentiment analysis, a subtask of opinion mining, classifies textual content, such as tweets or user reviews, by emotional tone (positive, negative, or neutral) [10]. It involves analyzing opinions, attitudes, and emotions expressed in written language, often applied to social media posts, customer reviews, and survey responses. By categorizing sentiments as positive, negative, or neutral, sentiment analysis helps organizations and researchers understand public perception, monitor brand reputation, and detect emerging trends. Among the most commonly adopted methods are Support Vector Machines (SVM) and Naive Bayes (NB), known for their efficiency and performance in short-text classification tasks. For example, [PERSON] and [PERSON] [11] used SVM and delta TF-IDF for sentiment classification and event detection related to urban transport in Milan. [PERSON] et al. [12] applied SVM to analyze public sentiment on various public transportation services in Jakarta, achieving 78.12% accuracy. Additionally, [PERSON] et al. [13] used Naive Bayes Classifier (NBC) for sentiment analysis of tweets related to Gojek online transportation services, achieving 81% accuracy. [PERSON] et al. [14] used Naive Bayes Classifier for analyzing sentiments toward major transportation services in Greater Jakarta. Several studies explored hybrid or comparative approaches. [PERSON] and [PERSON] [15] used SVM, Naive Bayes, and Decision Tree algorithms for sentiment classification of online transportation services (Gojek and Grab) in Indonesia. [PERSON] et al. [16] compared SVM, Random Forest, and Multinomial Naive Bayes (MNB) for sentiment analysis of Indonesia's Commute Line services, with SVM achieving the highest accuracy (85%). Overall, SVM demonstrates consistent performance in balanced datasets, achieving accuracies between 72.33% and 86%, while NB is effective for smaller datasets, as shown in studies analyzing sentiment toward COVID-19 transmission risks and public transportation services. Deep learning techniques have recently gained prominence due to their improved ability to capture linguistic subtleties. For instance, [PERSON] et al. [17] used deep learning models, such as CNNs and BiLSTMs, for road passability detection during floods, leveraging metadata and image analysis. [PERSON] et al. [18] applied CNNs for classifying sentiments in French tweets related to transportation in Ile-de-France. [PERSON] et al. [19] then used the same model for sentiment classification of Instagram comments related to online transportation services in Indonesia, both achieving 94% accuracy. Even higher performance was observed with hybrid models. [PERSON] et al. [20] employed a hybrid BiLSTM model with an attention mechanism, achieving 98.7% accuracy in sentiment classification for online ride-hailing apps. In the context of the COVID-19 pandemic, [PERSON] and [PERSON] [21] utilized the BERT model for sentiment analysis of Twitter data, focusing on logistics trends in Japan, and [PERSON] et al. [22] applied BERT-based models to classify travel modes and sentiments in New York City. Despite the rise of neural models, lexicon-based approaches continue to be used due to their simplicity and low computational cost. [PERSON] et al. [23] applied lexicon-based sentiment analysis with AFINN to analyze public transportation satisfaction in Malaysia, achieving high accuracy when combined with SVM, and [PERSON] et al. [24] leveraged AFINN lexicon to analyze sentiment in geotagged tweets, focusing on urban transportation issues like congestion and road quality in Indian cities. Otherwise, a study conducted by [PERSON] et al.