Abstract:

Latest trends in information technology development with the possibilities of big data processing makes it possible to conduct interdisciplinary technology-psychology research to build and verify formal psychometric models for software that can predict certain forms of personal success. Previously, we presented an approach to development of automated system for predicting the academic success of students based on data from the information-analytical system of the University and from their profiles on social networks. The system was developed within the problem framework of creating a psychometric model of success, which is based on a selected set of cognitive behavioral predictors of personal activity. In this paper we present an application of the same approach to prediction of another form of personal success, namely professional success, which includes job career, achievement of certain specialist grades after main graduation, job positions and professional objectives. This is done using open data from the largest Russian online recruitment site along with texts from profiles on social networks. Again, classical text indexing and word frequency characteristics analysis was used for information retrieval, and content analysis method of category grid extraction was applied to identify cognitive-behavioral predictors of professional success, such as the most typical text topics for groups of relatively successful, average and unsuccessful persons. The developed system was tested on prepared dataset, the results and conclusions are presented.

Keywords:

Natural language processing, content analysis, topic extraction, professional psychometry.