Text mining in career studies: Generating insights from unstructured textual data

Text data pertaining to peoples’ careers have proliferated in the past few decades. Due to the digitization of job search, recruitment, and the development of HR systems, it is relatively easy to access and obtain large datasets containing information about jobs or other work-related information at the micro (individual), meso (institutional), and macro (regional, national and global) levels, or some combination thereof. Examples of text data that may be used to study careers include (auto)biographies, résumés, posts in professional social networking sites, online job boards, public surveys, interview transcripts, personal diary entries, and even academic publications. Of particular interest are job vacancies, as aside from education and job experience, they also contain information about individuals’ roles, responsibilities, knowledge, skills, and abilities, which comes with the promise of adding specificity and context to the career domain, which has come to be dominated by reductionist and generalist approaches to operationalizing key constructs. Online forums and social media also provide data relevant to the study of careers since employees use these platforms to voice their ongoing opinions and sentiments about their past and
present employers.