IDENTIFICATION OF MBTI (MYERS-BRIGGS TYPE INDEX) HUMAN TYPE USING TEXT ON SOCIAL NETWORKS BASED MACHINE LEARNING

2021

Полный текст Открытый доступ: Hybrid Метаданные (CSL-JSON)
DOI 10.52512/2306-5079-2021-86-2-136-144
Достоверность данных 70 средняя
Как считается эта оценка

Складывается из проверяемых признаков самой записи: есть ли у статьи DOI, сколько независимых источников подтвердили её данные, заполнены ли обязательные поля и насколько свежо последнее подтверждение. Снижают оценку расхождения между источниками и отсутствие идентификатора. Значение от 80 означает, что запись опубликована без дополнительной проверки; от 60 до 79 — что она опубликована с пониженной достоверностью и ручная проверка желательна. Запись, выверенную редактором вручную, пересчёт не опускает ниже 90. Это оценка наших данных о статье, а не оценка самой статьи.

Аннотация

This study aims to create a classifier using machine learning methods that determine the psychological type of people based on the text published on social networks according to the Myers-Briggs Type Index classification. The article is based on the implementation of automation of the task of determining the personality type using machine learning, with an explanation for determining the characteristics of a person using the MBTI personality indicator. The methods of logistic regression, random forest and support vector machines were used, and a literary analysis of similar works was carried out. The article presents the progress of research work and the results of each classifier, as well as an analysis of the approaches used. In the context of the current quarantine restrictions, such studies can be of great help in the selection of personnel in companies due to the transition of people to an online format of work, since the study involves determining the personal qualities of people based on their posts in social networks. In this paper, the most effective machine learning algorithms for the Kazakh language, which are simple to use and do not require a lot of computing power, were used and, accordingly, the results of the work for each method were presented, among these methods, the accuracy and reliability of the classifier for the Kazakh language by the method of support vectors were at a good level.