用风格测量法和机器学习技术估算智商：综述

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2022-06-09 DOI:10.26599/BDMA.2022.9020002

Glory O. Adebayo;Roman V. Yampolskiy

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引用次数: 4

摘要

一个多世纪以来，心理学家一直致力于量化一个人的智力。使用触笔法估计智商的领域一直是一个发展中的研究领域，在触笔法分析中使用机器学习估计智商的有效性已在文献中得到证明，其结论表明使用大型数据集可以提高估计质量。由于这一研究领域缺乏大型数据集，导致很少有书面文本中IQ估计的出版物。在这篇论文中，我们回顾了在智商估计方面所做的研究，以及在使用风格测量法对作者进行分析方面所进行的研究，我们得出的结论是，基于智商估计和作者风格测量法的成功，如果使用正确的数据集，使用风格测量术对书面文本进行智商估计的研究将产生良好的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.