# Automated Identification of Sensitive Financial Data Based on the Topic Analysis
## 2 Methodology  
Figure 1 shows the overall architecture of the data security classification and the related method. The proposed method utilizes Jieba segmentation (Jieba is a widely-used Chinese text segmentation tool, known for its efficiency and support for various segmentation modes in natural language processing applications) to process raw text, generating a high-frequency word corpus containing both semantic and frequency information, thereby enhancing the efficiency and generalization ability of the model. Subsequently, the skip-gram model processes the high-frequency corpus, creating a word vector space. Through deduplication, vector representations of high-frequency words are obtained, serving as the training set for the K-means clustering algorithm. To improve the accuracy of sensitivity level classification, industry-expert-selected theme words are employed as the initial centroids for the K-means clustering, achieving a precise classification of the sensitivity levels in the text.  
The method takes the original text as input and generates a word segmentation corpus through Jieba word segmentation processing [7]. The word segmentation corpus contains all the semantic information and word frequency information in the original text. The low-frequency words in the word segmentation corpus are identified through frequency statistics [8]. Subsequently, all instances of such words are removed from the corpus. The obtained high-frequency word database can improve the generalization ability of subsequent training models. The skip-gram model [9] further processes the high-frequency corpus, which generates the word vector space of the corpus. The model additionally incorporates the underrepresented high-frequency vocabulary into the corpus. The vector representation of all high-frequency word data obtained serves as the training set for the subsequent clustering algorithm.  
\(k\) points are usually randomly selected as the initial centroid in K-means clustering [10]. This selection method is dependent on the initial value of the centroid [11], and inappropriate centroids may cause the algorithm to fall into a local optimal solution. Even descriptive texts of the same sensitivity level may have semantic differences. For example, \"customer name\", \"customer ID card number\", and \"net investment asset value\" are all text of the same sensitivity level. However, there is a significant semantic difference between \"net investment asset value\" and the other two fields, and the clustering algorithm is likely to divide them into different clusters. The initial centroid selection to address this problem requires industry professionals who can precisely choose multiple keywords. Each keyword is assigned a sensitivity level, such as assigning the keywords \"customer\" and \"asset\" to the same level of sensitivity. These keywords will be utilized for clustering to generate multiple topic domains. The sensitivity level of the fields contained in each subject field is the same as that of the subject word, which can precisely divide the sensitivity level.  
The keywords selected by experts need to be entered before clustering to improve the accuracy of sensitivity classification. The sensitivity level of keywords is scored, and a labeled subject centroid is obtained as the initial centroid of the K-means clustering algorithm. Finally, the K-means clustering algorithm divides the data space into multiple subject domains through iterative optimization of the training set. Each subject domain corresponds to a subject word, and the sensitivity level of all corpuses in that domain is the sensitivity level of the subject word. The sensitivity classification results of all the original texts are obtained in this way.  
Figure 1: Overall architecture of data security classification.