預測潛在違法可能性公司之系統

Prediction Illegal Companies System

惠霖、李沅紘、尤崑名、彭景楠
Lin Hui, Y. H. Lee, K. M. Yu and C. N. Peng 淡江大學 資訊創新與科技學系軟體工程組


摘要

本系統希望提供求職者一份參考數據,可以避免掉高風險的違法公司,以保障自己的勞工權益;或是幫助勞動部在人力不足之下縮小檢查對象,提高效率。本系統是透過對於公司的評論,找尋負面評論的「主題」是否關於勞基法,此方法不需討論負評詞彙,計算出是否有可能違法勞基法的可能性。

關鍵字:TF-ID、勞基法、資料探勘、文字探勘、主題探勘。

ABSTRACT

The system provides reference data to labors, so they can avoid to work in high risk illegal companies for protecting their right and interests; otherwise, the system help the ministry of Labor to narrow down companies inspection list when the ministry lacks labors. Through searching for a special topic, such as labor laws, of negative comments, our system will calculate weight of potation illegal companies.

Keywords: TF-IDF; Labor Law; Data Mining, Text Mining; Topic Mining