Smarter people analytics with organizational text data: Demonstrations using classic and advanced NLP models

Feng Guo, Christopher M. Gallagher, Tianjun Sun, Saba Tavoosi, Hanyi Min

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Recent developments in text mining and natural language processing (NLP) have paved a new way for analysing text data. These techniques are particularly useful for human resource management (HRM) due to the large amount of text information in the field. This paper adds to the literature by introducing and demonstrating steps of using NLP. Two demonstrations are presented: Demonstration One illustrates how simple and straightforward Bag-of-Word models applied on textual comments can be used to predict numerical ratings of companies; Demonstration Two shows how personality (self-reported scores on the Big Five) can be predicted from situational interview questions through more complex Doc2Vec algorithms. Together, these demonstrations show that both simple and complex techniques are effective tools in predicting organizational outcomes. Accessible syntax and guides for beginners are also provided.

Original languageEnglish (US)
JournalHuman Resource Management Journal
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Organizational Behavior and Human Resource Management

Fingerprint

Dive into the research topics of 'Smarter people analytics with organizational text data: Demonstrations using classic and advanced NLP models'. Together they form a unique fingerprint.

Cite this