###### Abstract
The proposed approach, RoBERTa, achieved the highest accuracy (97%), but Word2 Vec combined with BiLSTM excelled in handling tweet-length limitations by effectively capturing word semantics [3]. The study highlights that in short texts like tweets, Word2 Vec with BiLSTM can outperform more complex models that rely on longer context with an F1 of 96%, exceeding BERT, Word2 Vec, GloVe, and XLNet. Traditional methods such as Support Vector Machine (SVM) and Naive Bayes, as well as DL methods such as CNNs and RNNs, have been used widely in text classification [4]. There remain several lingering issues in the text classification problem. First, data sparsity has been addressed using both features from knowledge bases and part-of-speech tags as well as neural networks to transform words into vectors. However, both have limitations where explicit representations face the problem of data sparsity, and vector representations perform poorly on new and rare words, ignoring the is-A relation, which is important for short text understanding [1]. Second, the ambiguity within short, social media-based texts, compared to paragraphs or documents, does not have sufficient contextual information and suffers from limited word count and incomplete semantics, which leads to semantic ambiguity and poses a significant challenge for short text classification [5]. A problem in previous research on short text classification is the limited focus on integrating multiple embedding layers, such as character embedding, word embedding, and concept embedding, in a unified framework. Aiming to overcome the limitations of equal embedding by capturing richer semantic and syntactic information, leading to improved classification accuracy. Traditional methods like N-gram-based text categorization struggle with short texts due to data sparsity and lack of context, while SVM face challenges with scalability and capturing complex patterns. Naive Bayes suffers from an unrealistic independence assumption and performs poorly with imbalanced or rare data [6]. Existing approaches to traffic-related social media text classification face limitations, including a lack of multi-level embedding, poor context capture, and difficulty handling rare or new terms, reducing classification accuracy. Current models, such as RoBERTa, while achieving high accuracy (97%), struggle with tweet-length limitations and fail to address the semantic ambiguity in shorter contexts [3]. These gaps indicate a need for integrating character, word, and concept embeddings within a unified framework to enhance semantic and syntactic information capture, critical for improved classification [4]. DLCTC's use of multi-level embeddings and Probase [7] directly tackles these issues by enriching feature spaces and resolving semantic ambiguity, which are essential for accurate social media classification. The integration of various techniques, such as TextCNN, embedding-based methods, and BiLSTM, plays a crucial role in enhancing short-text classification. TextCNN captures semantic features within the text, showing adaptability for short texts. Embedding-based techniques at character, word, and concept levels provide robust semantic representations, helping models understand complex relationships and context. In DLCTC, character embeddings handle rare word issues, word embeddings provide broader contextual meaning, and concept embeddings clarify semantic nuances, enhancing classification accuracy. BiLSTM supports long-term dependencies and context, especially when paired with attention mechanisms that prioritize key parts of the input. This combination--TextCNN for feature extraction, embeddings for comprehensive representation, and BiLSTM for sequential context--creates a unified model leveraging linguistic features and external knowledge, significantly improving classification accuracy and robustness. While some progress has been made, many models still fall short in handling informal language, abbreviations, and contextual complexity of traffic-related social media data.