# Perceiving Conflict of Interest Experts Recommendation System Based on a Machine Learning Approach
## 3 Proposed Methodology
### Preprocessing Collected Data
#### 3.2.2 Keyword Extraction  
The keyword extraction process consists of two steps: (1) extracting a keyword list from the collected academic metadata, and (2) performing automatic keyword extraction based on the publications and project histories of the experts. In this study, the term frequency-inverse document frequency (TF-IDF) value for each keyword was generated by vectorizing the titles and abstracts of academic articles. The core keywords that effectively summarize the document were extracted by applying importance weights based on TextRank [30]. Furthermore, compound nouns that should be treated as one keyword in the document were processed by expanding conventional keyword extraction centered on single nouns. Stop word handling is essential in the TF-IDF [31] algorithm because it retains important words that represent the subject while filtering out high-frequency common words. Once the stop words were removed from all documents, the vocabulary size was reduced further by eliminating words that appeared less than three times. Since most publications are in Korean, KoNLPy ([[https://konlpy.org](https://konlpy.org)]([https://konlpy.org](https://konlpy.org)) (accessed on 13 August 2022)), which is a Korean morpheme analysis package, was used to remove stop words by word-class tagging after dividing sentences into morphemes. For example, \"A Study on the Behavior Modeling and Detection Method of Financial Fraud Using Ensemble Learning\" can be divided into word sets \"Behavior Modeling,\" \"Detection Method,\" \"Financial Fraud,\" and \"Ensemble Learning.\" The English text uses NLTK ([[https://www.nltk.org](https://www.nltk.org)]([https://www.nltk.org](https://www.nltk.org)) (accessed on 13 August 2022)) to separate sentences from the document text and to analyze and extract keywords. Figure 2: Document preprocessing steps. The TF-IDF indicates statistical values that represent the importance of words in a specific document to extract keywords. The TF-IDF of the kth word t in the jth document d is given as  
\[\text{TF}-\text{IDF}\big{(}\text{t}_{\text{k}},\text{ d}_{\text{j}}\big{)}\ =\ \text{TF}-\text{IDF}\ \big{(}\text{t}_{\text{k}},\text{ d}_{\text{j}}\big{)}\ \times\ \text{IDF} \tag{1}\]  
where TF represents the frequency of a specific word t in document d. Further, we assume that keywords that frequently appear in a document are more important. The TF is divided by the total frequency of all words for normalization as  
\[\text{TF}\big{(}\text{t}_{\text{k}},\text{ d}_{\text{j}}\big{)}\ =\ \frac{\text{f}_{\text{k},\text{ j}}}{\max_{\text{z}}\text{f}_{\text{z},\text{ j}}} \tag{2}\]  
However, a high-frequency word can correspond to a stop word. Therefore, IDF, which digitalizes the frequency of word t in a document set d, is calculated. The total number of documents is divided by the number of documents containing word t, and its log is obtained as  
\[\text{IDF}\big{(}\text{t}_{\text{k}},\text{ d}_{\text{j}}\big{)}\ =\ \log\frac{\text{N}}{\text{n}_{\text{k}}} \tag{3}\]  
where N denotes the total number of documents and n\({}_{\text{k}}\) represents the number of documents in which the kth word t appears. Here, one is added to the denominator to prevent the case where no document contains word t. Words appearing in fewer documents have a higher IDF and are more likely to be extracted as keywords. Each word can be represented by a word weight between zero and one through TF-IDF. A document-keyword matrix is established when the Top-N words with high word weights are selected as keywords through the above process, as summarized in Table 3.  
A word weight graph was generated using the above document-keyword matrix and the TextRank algorithm was applied. Each TextRank algorithm creates a graph with words as nodes and the simultaneous appearance relationship of each word as edges. Scores and rankings are assigned to word nodes, which are the vertices of the edge graph.