## 2 Literature Review
### Short Text Classification Methods for Social Media Traffic Data  
The analysis of traffic data across various studies highlights the potential of developed methods on ITS. Early studies, such as the study by [PERSON], [PERSON], and [PERSON], treated traffic event detection as a slot-filling problem. They developed a model combining LSTM and CRF, which performed well on Dutch datasets with accuracies above 97% [25]. However, they noted the necessity for enhanced techniques to capture the entire context within each tweet to improve detection accuracy. Meanwhile, [PERSON] explored the use of multi-modal generative adversarial networks (mmGAN) for traffic event detection, integrating data from sensors and social media [26]. Their model demonstrated superior performance compared to others, although they identified the need to apply attention mechanisms to improve the temporal modeling of multi-modal data. Moving into more recent research, [PERSON] combined doc2 vec, TF-IDF, and BERT embeddings to classify tweets related to traffic. Despite challenges with informal language and abbreviations, their study showed the evolving methodologies in improving the detection and classification of traffic-related events, highlighting the importance of precise handling of informal text in social media data [27]. [PERSON] and [PERSON] [28] explored methods to improve the classification of road traffic incident messages on X. They focused on balancing imbalanced datasets through techniques like under-sampling, oversampling, Markov chains, and Bi-LSTM [28]. The results indicated that while under-sampling and oversampling did not perform as well as the baseline, the Bi-LSTM method provided the best classification performance, with an F1-score of 0.63, outperforming the baseline by 15.44%. They concluded that Bi-LSTM is particularly suitable for classifying traffic report messages that resemble time series data. Recent studies have further advanced these methodologies. For instance, [PERSON] and [PERSON] utilized X data and deep learning techniques, notably RoBERTa, and achieved a high accuracy of 97% in classifying traffic events [3]. However, they encountered challenges with Named Entity Recognition (NER), as it often misclassified location names, indicating a need for improvements in location extraction from textual data. Moreover, several novel approaches have been proposed to address challenges like semantic sparsity, feature extraction, and label distribution. [PERSON] et al. introduced a knowledge-enhanced, soft-verbalizer-based prompt-tuning method for multi-label short-text classification, which addresses data sparsity and the long-tail distribution of labels by incorporating external knowledge and leveraging separating soft verbalizers. This approach outperformed baseline methods across benchmark datasets by optimizing prompt templates and soft verbalizers, which helped bridge the pre-training and downstream task gaps typical in language models [29]. [PERSON] et al. proposed an approach that combines BERT embeddings with part-of-speech features for dynamic word vector training, capturing long-distance dependencies in text. This integration enriched semantic recognition and achieved improved classification accuracy on the THUCNews dataset, showcasing its effectiveness in handling semantic insufficiency and sparse feature representation [30]. [PERSON] et al. explored a heterogeneous graph-convolution-network-based approach for short-text classification, enhancing feature representation by linking text with entity and word nodes. SHGCN, tested on datasets like AGNews and R52, demonstrated superior performance by combining BERT embeddings with BiLSTM for capturing deep textual relationships [31]. Similarly, [PERSON] et al. advanced text classification with an adaptive segmentation model, designed to adjust segmentation according to text length and embedding importance.