Personality Traits Prediction from Text via Machine Learning

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI:10.1109/AIC55036.2022.9848937

Alessandro Bruno, Gurmeet Singh

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引用次数: 3

Abstract

Social media platforms have been expanding their user bases. For example, LinkedIn counts 917 million monthly visitors, while Twitter has 3.62 billion monthly visitors. YouTube has 22.77 billion monthly visitors, and Instagram has 2.86 billion monthly visitors. Reports confirm data size increase of the social media networks above by 20–30% every day. With the spread of COVID-19, the same platforms have been broadly used by the worldwide collectiveness to socialize and stay amongst people. Analyzing text from Social Networking sites helps recognize individuals' personality traits automatically. A person's personality refers to their unique characteristics that shape their habits, behaviour, attitude, and cognitive tendencies. In this work, several machine learning techniques are surveyed to estimate personality traits from input text using the Myers-Briggs Type Indicator (MBTI) model. Experiments are run over a freely accessible dataset from Kaggle. In addition, techniques such as tokenization, word stemming, stop word elimination, and feature selection, utilizing TF-IDF, are used to analyze personality traits further.

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通过机器学习从文本中预测人格特征

社交媒体平台一直在扩大其用户基础。例如，LinkedIn的月访问量为9.17亿，而Twitter的月访问量为36.2亿。YouTube月访问量为227.7亿，Instagram月访问量为28.6亿。报告证实，上述社交媒体网络的数据量每天以20-30%的速度增长。随着新冠肺炎疫情的蔓延，世界各地的人们广泛使用相同的平台进行社交和交流。分析来自社交网站的文本有助于自动识别个人的个性特征。一个人的个性是指他们独特的特征，这些特征塑造了他们的习惯、行为、态度和认知倾向。在这项工作中，研究了几种机器学习技术，使用迈尔斯-布里格斯类型指标(MBTI)模型从输入文本中估计人格特征。实验是在Kaggle免费访问的数据集上进行的。此外，利用TF-IDF，使用标记化、词干提取、停止词消除和特征选择等技术进一步分析人格特征。

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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2022 IEEE World Conference on Applied Intelligence and Computing (AIC)

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