期刊
ORGANIZATIONAL RESEARCH METHODS
卷 21, 期 3, 页码 733-765出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1094428117722619
关键词
text mining; dimensionality reduction; clustering; topic modeling; classification; validation; job analysis
资金
- European Commission through Marie-Curie Initial Training Network EDUWORKS [PITN-GA-2013-608311]
- Society of Industrial and Organizational Psychology Sidney A. Fine Grant
Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.
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