結合詞向量技術與分群演算法於信用卡商戶名稱辨識

Combining Word Vector Technique and Clustering Algorithm for Credit Card Merchant Detection

透過客戶消費資料萃取相關之使用者行為，是蒐集客戶資訊的方式之一。現行文字探勘的領域中，大多以文本分類之相關研究為主，顯少有文本分群之研究主題。從非結構化之交易消費說明中，找尋字詞之間的關係，運用不同詞向量技術，突破分類需事先區分條件之限制，建立自動化辨識分析方法，提升分群之準確率。在本研究中將以銀行信用卡交易消費說明內容，進行Jieba中文斷詞並採用Word2Vec特徵值萃取，搭配基於密度分群法(DBSCAN)和階層分群法，交叉組合進行實驗。預測結果以MUC、B3和CEAF之F1平均值67.58%較為顯著。

Extracting relevant user behaviors through customer’s transaction description is one of the ways to collect customer information. In the current text mining field, most of the researches are mainly study text classification, and only few study text clusters. Find the relationship between letters and words in the unstructured transaction consumption description. Use Word Embedding and text mining technology to break through the limitation of classification conditions that need to be distinguished in advance, establish automatic identification and analysis methods, and improve the accuracy of grouping. In this study, use Jieba to segment Chinese words, were based on the content of credit card transaction description. Feature extractions of Word2Vec, combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Hierarchical Agglomerative Clustering, crosscombination experiments. The prediction results of MUC, B3 and CEAF's F1 average of 67.58% are more significant