Text mining in career studies: generating insights from unstructured textual data
Vladimer Kobayashi, Stefan T. Mol, Jarno Vrolijk, Gábor Kismihók · Edward Elgar Publishing eBooks · 2021
The abundance of unstructured data, primarily consisting of text, coupled with the development of modern techniques to analyze them, offer new opportunities for theory generation, theory testing, and contextualization within the field of career studies. This chapter explains the process of text mining as it may be applied to the study of careers, starting with text preprocessing and ending with the application of analytical techniques. Both conventional techniques such as latent semantic and principal components analysis, and more novel text analytical techniques such as word embeddings are discussed with the hope of inspiring careers researchers to further embrace text analysis as a means to answer novel research questions. Furthermore, this chapter elaborates on how to evaluate the validity of information extracted from text. In order to illustrate the text analytical process, we provide an example that leverages an abundant and growing source of data that may be of interest to career researchers, namely, vacancy data. In the example, vacancy data are used to identify those knowledge, skills and abilities that (co)determine the salary associated with particular occupations. We end the chapter by providing a guide on how text mining can be applied to study existing and new career concepts.