Sentimental Analysis of Placement Data for Job Classification by Students Using Naive Bayes in Comparison with Support of Vector Machine

Characterizing the work that people do on their jobs is a longstanding and core issue in labour economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labour costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labour market. This application examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupation. For this classification we use Naïve Bayes, Random forest, Decision Tree, K nearest neighbour and Support vector machine learning algorithms.  

Keywords: Machine learning approaches, Traditional approaches, Classification, Computational tools.