## 4 Data Modeling  
### Text Mining  
#### 4.1.1 Data Pre-Processing  
After preprocessing and structuring the text data, the structured text data is integrated with the numeric data to construct an integrated data set. Of the 260 projects initially collected, text data was collected for the remaining 255 projects, excluding five projects with bid risk percentages of more than \(\pm 35\%\). The collected text data comprise the pre-bid RFI information of each project, including bidder's inquiries about unclear and risk information contained in the bidding document. However, to the process of manually reviewing all of the pre-bid RFI information for 255 projects is time-consuming and inefficient. Thus, text mining using R programming (ver. 3.4.1) was performed to analyze the types of pre-bid RFI information of each project. Text mining refers generally to the process of extracting interesting and non-trivial patterns or knowledge from unstructured text documents [21]. One of the reasons for applying text mining in this study is to change the text data format to a structural vector format in order for integrated data analysis. To perform text mining on the pre-bid RFI document, appropriate preprocessing is required to ensure computers are able to automatically recognize the textual document. In other words, the unstructured information should be structured to be understood by the computers, and the unnecessary information should also be excluded. Before text preprocessing a total of 243 projects were finally selected as analysis data of the study by excluding 12 project data that do not have pre-bid RFI among the collected project data. In the text mining, the basic structure for managing documents is the corpus, which is a set of text documents. In this study, the corpus refers to a set of 243 pre-bid RFI. The corpus can be generated from the text documents imported from a directory, vector, or data frame in R. Each document in the corpus contains unnecessary information for analysis such as punctuation, symbols, and white spaces. By removing this unnecessary information, we established a basic environment for data analysis. In addition, words that appear in the pre-bid RFI document, such as 'inquiry', 'contractor'  
Figure 3: Research model process. and 'Caltrans', do not themselves have any special meaning for analysis, but appear repeatedly in all of the documents. Thus, the words were recognized as stop words for pre-bid RFI and were eliminated for effective analysis and improvement of processing speed. Table 1 shows the preprocessing results of text data. As a result of the preprocessing, the total number of words in the corpus decreased from 5009 terms to 3948 terms. The value indicating the sparsity of the words in the documents is 98%, indicating that only 2% of the words occur repeatedly, and 98% of the remaining words occur at a relatively low frequency. After removing the unnecessary information of the corpus through the preprocessing process, a term-document matrix is created that converts unstructured text data into a structured form. The term-document matrix is a matrix that quantifies the frequency of words that are contained in each document and can be constructed as shown in Equation (2). \[term-document\ matrix=\left( \begin{array}{cccc}T_{1}&T_{2}&T_{3}&\ldots&T_{t }\\ D_{1}&w_{11}&w_{12}&w_{13}&\ldots&w_{1t}\\ D_{2}&w_{21}&w_{22}&w_{23}&\ldots&w_{2t}\\ D_{3}&w_{31}&w_{32}&w_{33}&\ldots&w_{3t}\\ \ldots&\ldots&\ldots&\ldots&\ldots&\ldots&\ldots\\ D_{n}&w_{n1}&w_{n2}&w_{n3}&\ldots&w_{nt}\\ \end{array} \right) \tag{2}\]  
However, even after preprocessing, about 4000 terms are still included in the corpus, and not all of these words have equal importance. Therefore, it is necessary to select words having a high priority according to their weight. Term frequency-inverse document frequency' (tf-idf) is the most representative weighting for priority of words. Tf-idf considers the frequency of words in various documents as well as the frequency of words in each document.