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Open Access

Estimating Intelligence Quotient Using Stylometry and Machine Learning Techniques: A Review

Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA
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Abstract

The task of trying to quantify a person’s intelligence has been a goal of psychologists for over a century. The area of estimating IQ using stylometry has been a developing area of research and the effectiveness of using machine learning in stylometry analysis for the estimation of IQ has been demonstrated in literature whose conclusions suggest that using a large dataset could improve the quality of estimation. The unavailability of large datasets in this area of research has led to very few publications in IQ estimation from written text. In this paper, we review studies that have been done in IQ estimation and also that have been done in author profiling using stylometry and we conclude that based on the success of IQ estimation and author profiling with stylometry, a study on IQ estimation from written text using stylometry will yield good results if the right dataset is used.

Keywords

stylometryIQ estimationauthorship attributionintelligenceIQauthor profilingmachine learning

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Big Data Mining and Analytics
Volume 5 Issue 3,
September 2022
Pages 163-191
DOI: 10.26599/BDMA.2022.9020002
Cite this article:
Adebayo GO, Yampolskiy RV. Estimating Intelligence Quotient Using Stylometry and Machine Learning Techniques: A Review. Big Data Mining and Analytics, 2022, 5(3): 163-191. https://doi.org/10.26599/BDMA.2022.9020002

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Received: 27 June 2021
Revised: 03 December 2021
Accepted: 21 January 2022
Published: 09 June 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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