Towards an Automated Analysis of the Online Supply Chain of Novel Psychoactive Substances

There is a huge online data about job descriptions which has been entered by job seekers and job holders that can be utilized to give insight into the current state of jobs. Employers also produce large volume of vacancy data online which can be exploited to portray the current demand of the job market. When preparing job vacancies, taking into account the information contained in job descriptions, and vice versa, the likelihood of getting the bidirectional match of a job description and a vacancy will be improved. To improve the quality of job descriptions and job vacancies, a mediating system is required that connects and supports job designers and employers, respectively. In this paper, we propose a framework of an automatic bidirectional matching system that measures the degree of semantic similarity of job descriptions provided by job-seeker, job-holder or job-designer against the vacancy provided by employer or job-agent. The system provides suggestions to improve both job descriptions and vacancies using a combination of text mining methods.