AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Big Data Mining and Analytics Article
PDF (2.1 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
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
Show Author Information

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

machine learningintelligencestylometryIQ estimationauthorship attributionIQauthor profiling

References

[1]
P. Hallinan, Book review: Psychological testing (5th edn), Aust. Educ. Dev. Psychol., vol. 2, no. 2, p. 18, 1985,
Crossref Google Scholar
[2]
M. Lewis and P. Scale, RATIO IQ, https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470373699.speced1103,1983.
[3]
K. Cherry, Alfred Binet and the Simon-Binet intelligence scale, https://www.verywellmind.com/alfred-binet-biography-2795503, 2020.
[4]
E. Byington and W. Felps, Why do IQ scores predict job performance? An alternative, sociological explanation, Res. Organ. Behav., vol. 30, pp.175-202, 2010.
Crossref Google Scholar
[5]
Historical importance of ASVAB testing, https://asvabmilitarytest.com/history-of-asvab-test, 2022.
[6]
F. J. Tweedie, S. Singh, and D. I. Holmes, Neural network applications in stylometry: The federalist papers, Comput. Hum., vol. 30, no. 1, pp. 1-10, 1996.
Crossref Google Scholar
[7]
N. Ali, M. Hindi, and R. V. Yampolskiy, Evaluation of authorship attribution software on a chat bot corpus, in Proc. 2011 23r⁢d Int. Symp. Information, Commun. Autom. Technol., Sarajevo, Bosnia and Herzegovina, 2011, pp. 1-6.
Crossref Google Scholar
[8]
N. Ali, D. Schaeffer, and R. V. Yampolskiy, Linguistic profiling and behavioral drift in chat bots, CEUR Workshop Proceedings, vol. 841, pp. 27-30, 2012.
Google Scholar
[9]
R. V. Yampolskiy, N. Ali, D. D’Souza, and A. A. Mohamed, Behavioral biometrics, Int. J. Nat. Comput. Res., vol. 4, no. 3, pp. 85-118, 2014.
Crossref Google Scholar
[10]
R. J. Sternberg, Intelligence, Dialogues Clin. Neurosci., vol. 14, no. 1, pp. 19-27, 2012.
Crossref Google Scholar
[11]
V. Čavojová and E. B. Mikušková, Does intelligence predict academic achievement? Two case studies, Procedia - Soc. Behav. Sci., vol. 174, pp. 3462-3469, 2015.
Crossref Google Scholar
[12]
L. Wang, C. -Y. Wee, H. -I. Suk, X. Tang, and D. Shen, MRI-based intelligence quotient (IQ) estimation with sparse learning, PLoS One, vol. 10, no. 3, p. e0117295, 2015.
Crossref Google Scholar
[13]
M. Badar, M. Haris, and A. Fatima, Application of deep learning for retinal image analysis: A review, Comput. Sci. Rev., vol. 35, p. 100203, 2020.
Crossref Google Scholar
[14]
M. Brammer, The role of neuroimaging in diagnosis and personalized medicine-current position and likely future directions, Dialogues Clin. Neurosci., vol. 11, no. 4, pp. 389-396, 2009.
Crossref Google Scholar
[15]
C. J. Price, S. Ramsden, T. M. H. Hope, K. J. Friston, and M. L. Seghier, Predicting IQ change from brain structure: A cross-validation study, Dev. Cogn. Neurosci., vol. 5, pp. 172-184, 2013.
Crossref Google Scholar
[16]
T. Ohtani, P. G. Nestor, S. Bouix, Y. Saito, T. Hosokawa, and M. Kubicki, Medial frontal white and gray matter contributions to general intelligence, PLoS One, vol. 9, no. 12, p. e112691, 2014.
Crossref Google Scholar
[17]
F. J. Navas-Sánchez, Y. Alemán-Gómez, J. Sánchez-Gonzalez, J. A. Guzmán-De-Villoria, C. Franco, O. Robles, C. Arango, and M. Desco, White matter microstructure correlates of mathematical giftedness and intelligence quotient., Hum. Brain Mapp., vol. 35, no. 6, pp. 2619-2631, 2014.
Crossref Google Scholar
[18]
K. L. Narr, R. P. Woods, P. M. Thompson, P. Szeszko, D. Robinson, T. Dimtcheva, M. Gurbani, A. W. Toga, and R. M. Bilder, Relationships between IQ and regional cortical gray matter thickness in healthy adults, Cereb. Cortex, vol. 17, no. 9, pp. 2163-2171, 2007.
Crossref Google Scholar
[19]
G. S. P. Pamplona, G. S. Santos Neto, S. R. E. Rosset, B. P. Rogers, and C. E. G. Salmon, Analyzing the association between functional connectivity of the brain and intellectual performance, Front. Hum. Neurosci., vol. 9, p. 61, 2015.
Crossref Google Scholar
[20]
H. E. H. Pol, H. G. Schnack, D. Posthuma, R. C. W. Mandl, W. F. Baaré, C. V. Oel, N. E. V. Haren, D. L. Collins, A. C. Evans, K. Amunts, et al., Genetic contributions to human brain morphology and intelligence, J. Neurosci., vol. 26, no. 40, pp. 10235-10242, 2006.
Crossref Google Scholar
[21]
A. Arya and M. Manuel, Intelligence quotient classification from human MRI brain images using convolutional neural network, in Proc. 2020 12t⁢h Int. Conf. Comput. Intell. Commun. Networks (CICN), Bhimtal, India, 2020, pp. 75-80.
Crossref Google Scholar
[22]
R. Jiang, S. Qi, Y. Du, W. Yan, V. D. Calhoun, T. Jiang, and J. Sui, Predicting individualized intelligence quotient scores using brainnetome-atlas based functional connectivity, in Proc. 2017 IEEE 27t⁢h International Workshop on Machine Learning for Signal Processing (MLSP), Tokyo, Japan, 2017, pp. 1-6.
Crossref Google Scholar
[23]
S. Firooz and S. K. Setarehdan, IQ estimation by means of EEG-fNIRS recordings during a logical-mathematical intelligence test, Comput. Biol. Med., vol. 110, pp. 218-226, 2019.
Crossref Google Scholar
[24]
K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv: 1409.1556, 2015.
Google Scholar
[25]
Brainnetome Atlas, https://atlas.brainnetome.org/, 2021.
[26]
V. J. Schmithorst and S. K. Holland, Sex differences in the development of neuroanatomical functional connectivity underlying intelligence found using Bayesian connectivity analysis, NeuroImage, vol. 35, no. 1, pp. 406-419, 2007.
Crossref Google Scholar
[27]
N. Jaušovec, Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: An EEG study, Intelligence, vol. 28, no. 3, pp. 213-237, 2000.
Crossref Google Scholar
[28]
R. J. Haier, B. Siegel, C. Tang, L. Abel, and M. S. Buchsbaum, Intelligence and changes in regional cerebral glucose metabolic rate following learning, Intelligence, vol. 16, nos. 3&4, pp. 415-426, 1992.
Crossref Google Scholar
[29]
U. Basten, C. Stelzel, and C. J. Fiebach, Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network, Intelligence, vol. 41, no. 5, pp. 517-528, 2013.
Crossref Google Scholar
[30]
U. Basten, K. Hilger, and C. J. Fiebach, Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence, Intelligence, vol. 51, pp. 10-27, 2015.
Crossref Google Scholar
[31]
E. Stamatatos, N. Fakotakis, and G. Kokkinakis, Automatic text categorization in terms of genre and author, Comput. Linguist., vol. 26, no. 4, pp. 471-495, 2000.
Crossref Google Scholar
[32]
A. Abbasi and H. Chen, Applying authorship analysis to extremist-group web forum messages, IEEE Intell. Syst., vol. 20, no. 5, pp. 67-75, 2005.
Crossref Google Scholar
[33]
E. Stamatatos, A survey of modern authorship attribution methods, J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 3, pp. 538-556, 2009.
Crossref Google Scholar
[34]
A. Hendrix and R. Yampolskiy, Automated IQ estimation from writing samples, https://aws.amazon.com/publicdatasets/common-crawl/, 2017.
[35]
P. S. Abramov and R. V. Yampolskiy, Automatic IQ estimation using stylometric methods, in Handbook of Research on Learning in the Age of Transhumanism, S. Sisman-Ugur and G. Kurubacak, eds. Hershey, PA, USA: IGI Global, 2019, pp. 32-45.
Crossref
[36]
S. Argamon, M. Koppel, J. W. Pennebaker, and J. Schler, Automatically profiling the author of an anonymous text, Commun. ACM, vol. 52, no. 2, pp. 119-123, 2009.
Crossref Google Scholar
[37]
M. Koppel, J. Schler, and K. Zigdon, Determining an author’s native language by mining a text for errors, in Proc. 11t⁢h ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., Chicago, IL, USA, 2005, pp. 624-628.
Crossref Google Scholar
[38]
K. Alrifai, G. Rebdawi, and N. Ghneim, Arabic tweeps gender and dialect prediction: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-9, 2017.
Google Scholar
[39]
D. Kodiyan, F. Hardegger, S. Neuhaus, and M. Cieliebak, Author profiling with bidirectional RNNs using attention with GRUs: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-10, 2017.
Google Scholar
[40]
PAN at CLEF, https://pan.webis.de/, 2022.
[41]
F. Rangel, P. Rosso, M. Potthast, and B. Stein, Overview of the 5th author profiling task at PAN 2017: Gender and language variety identification in Twitter, CEUR Workshop Proc., vol. 1866, pp. 1-26, 2017.
Google Scholar
[42]
F. Rangel, P. Rosso, B. Verhoeven, W. Daelemans, M. Potthast, and B. Stein, Overview of the 4th author profiling task at PAN 2016: Cross-genre evaluations, CEUR Workshop Proc., vol. 1609, pp. 750-784, 2016.
Google Scholar
[43]
M. Koppel, S. Argamon, and A. R. Shimoni, Automatically categorizing written texts by author gender, Literary and Linguistic Computing, vol. 17, no. 4, pp. 401-412, 2002.
Crossref Google Scholar
[44]
J. A. Khan, Author profile prediction using trend and word frequency based analysis in text: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-7, 2017.
Google Scholar
[45]
K. Nishiyama, G. O. Adebayo, and R. Yampolskiy, Authorship identification of translational algorithms, in Proc. 2021 IEEE 15t⁢h International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2021, pp. 90-91
Crossref Google Scholar
[46]
, C. He, and K. Rasheed, Using machine learning techniques for stylometry, in Proc. Int. Conf. Artif. Intell. IC-AI’04, Las Vegas, NV, USA, 2004, pp. 897-903.
Google Scholar
[47]
Y. Adame-Arcia, D. Castro-Castro, R. O. Bueno, and R. Mu-ñoz, Author profiling, instance-based similarity classification: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-7, 2017.
Google Scholar
[48]
G. Kheng, L. Laporte, and M. Granitzer, INSA LYON and UNI PASSAU’s participation at PAN@CLEF’17: Author profiling task: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-11, 2017.
Google Scholar
[49]
M. Franco-Salvador, N. Plotnikova, N. Pawar, and Y. Benajiba, Subword-based deep averaging networks for author profiling in social media: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-10, 2017.
Google Scholar
[50]
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, Enriching word vectors with subword information, Trans. Assoc. Comput. Linguist., vol. 5, pp. 135-146, 2017.
Crossref Google Scholar
[51]
N. Schaetti, UniNE at CLEF 2017: TF-IDF and deep-learning for author profiling: Notebook for PAN at CLEF 2017, CEUR Workshop Proc., vol. 1866, pp. 1-11, 2017.
Google Scholar
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

1239

Views

160

Downloads

7

Crossref

3

Web of Science

7

Scopus

0

CSCD

Altmetrics
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/).
Return