## 2 Literature Review
### Related Work
#### 2.2.2 Textual Data Approaches  
Several recent domains have sought to apply text mining in unstructured data. For example, transportation studies utilized NLP and text mining techniques to extract insights from unstructured crash narratives. However, these studies focused primarily on addressing two research directions: (1) classification tasks and (2) extracting hidden trends from the unstructured data. The crash narratives may include valuable information that does not exist in the tabular analyses. This asset can provide contextual information by applying cutting-edge text mining techniques, resulting in a better understanding of crash severity outcomes to help policymakers develop prevention strategies. Remarkably, little research has focused on crash narratives to enhance the understanding of the factors influencing crash severity levels. Advances in machine learning have contributed significantly to text classification. Several machine learning-based studies have been conducted for text classification in traffic narratives. For example, [35] proposed an approach that combines text mining with interpretable machine learning to identify causal factors associated with injury severity. They adopted a variety of text mining and machine learning models such as neural networks, support vector machines, the naive Bayes algorithm, random forests, XGBoost, and logistic regression to classify crash severity. The neural network showed the best performance among the tested models. This proposed approach showed a new analytic perspective to explain key factors associated with high-severity crashes which cannot be obtained through classification tabular data analysis. However, textual data were converted into numeric vectors using TF-IDF representation. In contrast to word frequency vectorization, the TF-IDF approach ignores the words with low importance to provide a meaningful explanation. This study's limitation is that the bag-of-words method does not consider the contextual semantic relation between the words. Another study used three machine learning algorithms to find vehicles involved in hydroplaning crashes [36]. This study demonstrated the capability of text mining to detect vehicle crashes and provided a platform to understand interpretability in machine learning. XGBoost was found to be the most effective classifier. The benefit of this study is that the accuracy could be improved, and extra crash variables could be included to improve model performance. The author of ref. [37] presented a text mining-based approach to reveal contextual relationships in motor vehicle crash narratives based on word frequency. The study used Queensland traffic accidents between 2004 and 2005 to extract the causes of the crashes and determine the leading causes of those crashes. Their results were interesting in that they can help policymakers to better assess road crashes. However, this study did not consider severity analysis interpretation. In contrast to machine learning, deep learning models can extract complex characters from the word order of input data with superior classification accuracy. These models require word embeddings such as Word2 Vec, a two-layer neural network. Word embeddings are capable of extracting semantic relations in the text [38]. The author of ref. [39] developed a verb-based text mining algorithm to identify and extract the main verbs associated with vehicles' actions in a crash accident. The proposed approach was evaluated using 945 crash reports. The results showed that the extracted information is helpful for crash classifications and identifying the causes of the crash. The authors of ref. [40] conducted a study to predict secondary crashes. The authors developed a text mining approach to recognize secondary crashes from crash narratives. Four steps were used to process the data: tokenization, counting, vectorization, and normalization to convert the text narratives into vector structure. The logistic regression model achieved the highest accuracy of all the models used.