Big Data Analysis and Forecast of Employment Position Requirements for College Students

With the help of natural language processing and machine learning, we can analyze information online recruitment text posted by employer companies dig requirement features these employment positions, this is a meaningful work with practical value. However, existing methods for analyzing are too simple to withstand mass data on Internet, so paper aims study analysis forecast position requirements college students based big analysis. At first, preprocessed, keywords in extracted Term Frequency-Inverse Document Frequency (TF-IDF) algorithm Python Chinese word segmentation toolkit, words segmented using open source tool Word2Vec, uncounted identified Conditional Random Field (CRF) model. Then, compares occurrence probability skills learnt certain company all positions website, as find out core that match job required companies. last, builds XGBoost model students, verifies validity experimental results.