- Text Classification for Organizational Researchers
- A Tutorial
- Organizational Research Methods
- Volume | Issue number
- 21 | 3
- Pages (from-to)
- Number of pages
- Document type
- Faculty of Economics and Business (FEB)
- Amsterdam Business School Research Institute (ABS-RI)
Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this paper is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. In order to help researchers develop their own text classifiers, the R code associated with each step is presented in a tutorial. The tutorial draws from our own work on job vacancy mining. We end the paper by discussing how researchers can validate a text classification model and the associated output.
Keywords: Text classification, text mining, random forest, support vector machines, naive Bayes
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- This work was supported by the European Commission through the Marie-Curie Initial Training Network EDUWORKS (grant number
PITN-GA-2013-608311) and by the Society of Industrial and Organizational Psychology Sidney A. Fine Grant for Research on Job
Analysis, for the “Big Data Based Job Analytics project”.
An earlier version of this manuscript was presented as “Kobayashi, V.B., Berkers, H.A., Mol, S.T., Kismihók, G., & Den Hartog, D.N. (2015, August). Augmenting Organizational Research with the Text Mining Toolkit: All Aboard! In LeBreton, J.M (Chair), Big Data: Implications for Organizational Research, Showcase Symposium at the 75th Annual Meeting of the Academy of Management, Vancouver, BC, Canada.
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