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Text Mining in Organizational Research.

Vladimer B Kobayashi¹, Stefan T Mol¹, Hannah A Berkers¹

¹Leadership and Management Section, Amsterdam Business School, University of Amsterdam, Netherlands.

Organizational Research Methods

|June 9, 2018

Summary

This summary is machine-generated.

Text mining offers a quantitative approach to analyze large textual data for organizational research. This method accelerates knowledge discovery and enables testing research questions with rich, ecologically valid data.

Keywords:

classification clustering dimensionality reduction job analysis text mining topic modeling validation

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Area of Science:

Organizational Research
Data Science
Computational Social Science

Background:

Textual data is ubiquitous but underutilized in organizational research.
Traditional methods struggle with the volume and complexity of textual data.
Text mining provides a quantitative approach to analyze large text datasets.

Purpose of the Study:

To introduce organizational researchers to text mining principles and techniques.
To demonstrate how text mining can address organizational research questions.
To highlight the potential of text mining for knowledge discovery.

Main Methods:

Review of fundamental text mining logic and analytical stages.
Exploration of techniques: dimensionality reduction, distance/similarity computing, clustering, topic modeling, classification.
Illustration using a job analysis dataset of job vacancies.

Main Results:

Text mining enables the analysis of voluminous textual data, accelerating knowledge discovery.
It allows testing research questions with rich, contextualized, and ecologically valid data.
The article provides a practical illustration of the text mining process.

Conclusions:

Text mining offers a powerful, underutilized tool for organizational researchers.
Its application can extend the scope and rigor of organizational research.
The techniques discussed can be applied to various organizational research objectives.