Natural Computing and Machine Learning Laboratory, Mackenzie Presbyterian University, São Paulo, Brazil.
PLoS One. 2019 Mar 12;14(3):e0212844. doi: 10.1371/journal.pone.0212844. eCollection 2019.
Temperament and Psychological Types can be defined as innate psychological characteristics associated with how we relate with the world, and often influence our study and career choices. Furthermore, understanding these features help us manage conflicts, develop leadership, improve teaching and many other skills. Assigning temperament and psychological types is usually made by filling specific questionnaires. However, it is possible to identify temperamental characteristics from a linguistic and behavioral analysis of social media data from a user. Thus, machine-learning algorithms can be used to learn from a user's social media data and infer his/her behavioral type. This paper initially provides a brief historical review of theories on temperament and then brings a survey of research aimed at predicting temperament and psychological types from social media data. It follows with the proposal of a framework to predict temperament and psychological types from a linguistic and behavioral analysis of Twitter data. The proposed framework infers temperament types following the David Keirsey's model, and psychological types based on the MBTI model. Various data modelling and classifiers are used. The results showed that Random Forests with the LIWC technique can predict with 96.46% of accuracy the Artisan temperament, 92.19% the Guardian temperament, 78.68% the Idealist, and 83.82% the Rational temperament. The MBTI results also showed that Random Forests achieved a better performance with an accuracy of 82.05% for the E/I pair, 88.38% for the S/N pair, 80.57% for the T/F pair, and 78.26% for the J/P pair.
气质和心理类型可以被定义为与我们与世界的关系相关的先天心理特征，通常会影响我们的学习和职业选择。此外，了解这些特征有助于我们管理冲突、发展领导力、提高教学水平以及许多其他技能。气质和心理类型的分配通常是通过填写特定的问卷来完成的。然而，从用户社交媒体数据的语言和行为分析中也可以识别气质特征。因此，可以使用机器学习算法从用户的社交媒体数据中学习，并推断出他/她的行为类型。本文首先简要回顾了气质理论的历史，然后对从社交媒体数据预测气质和心理类型的研究进行了调查。接着提出了一个从 Twitter 数据的语言和行为分析中预测气质和心理类型的框架。该框架根据大卫·凯尔西的模型推断气质类型，根据 MBTI 模型推断心理类型。使用了各种数据建模和分类器。结果表明，使用 LIWC 技术的随机森林可以以 96.46%的准确率预测工匠气质，92.19%的守护者气质，78.68%的理想主义者气质，83.82%的理性气质。MBTI 的结果也表明，随机森林在 E/I 对的准确率为 82.05%，S/N 对的准确率为 88.38%，T/F 对的准确率为 80.57%，J/P 对的准确率为 78.26%。
