Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer Text

Abstract

Personality assessments are at present nearly entirely dependent on self-reports, and machine learning methods have been rarely applied to this field. This study used machine learning to predict people's self-reported proactive personalities. Based on a sample of 901 participants that used Weibo text and short answer text, the authors used five machine learning algorithms for classification: Support Vector Machine (SVM), XGboost, k-nearest neighbor (KNN), naïve Bayes, and logistic regression. Seven different indicators -Accuracy (ACC), F1-score(F1), Sensitivity(SEN), Specificity (SPE), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Area under Curve (AUC) -combined with hierarchical crossvalidation were also used to make the comprehensive evaluation of models. Based on this, we proposed a method to classify people's proactive personalities based on text mining technology. The results showed that the SVM and naïve Bayes outperformed the other methods on short texts (i.e., short answer texts) and mixed long texts (i.e., short answer & Weibo text). In the context of the texts used, mixed long text (i.e., short answer & Weibo text) improved and stabilized the indices, and this combination was the best choice of text for predicting proactive personality. In addition, the SVM was the most stable classifier in most situations, even on Weibo text that was not suitable for analysis as long text, and it also recorded good results in terms of accuracy, the F1-score, and the AUC.

A Survey: Naive Bayes’-Enhanced Text-Based Personality Analysis

“…This work underscores the need for more comprehensive investigations into multimodal datasets and the current reliance on self-reports in personality assessments. [2] In an article by Peng Wang and colleagues, published in IEEE in June 2021, Support Vector Machine (SVM), knearest neighbor (KNN), Naive Bayes, and Logistic Regression were evaluated for personality analysis, with the utilization of a Random Forest Classifier. Unfortunately, detailed results were not provided.…”

“…At its core, the Naive Bayes algorithm is employed to analyze text responses by calculating the probability of a text belonging to specific personality traits. Despite its simplicity, Naive Bayes has proven highly effective in text analysis tasks, making it a suitable choice for our system [2]. By implementing Naive Bayes, we aim to create a fair and unbiased personality assessment tool with applications for individuals, organizations, and researchers.…”

Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

“…The model evaluation criterion used in this paper was the accuracy rate (ACC) [ 30 , 31 , 32 ]. The model evaluation metrics of classification algorithms are often measured by confusion matrices, as shown in Table 4 .…”

Designing answer sets for Thai advisory chatbot with different talking styles for COVID-19 pandemic

“…In [13], the authors did the studies about training the text classification models for proactive personality prediction by applying five machine learning algorithm; NB, KNN, SVM, XGBoost and Logistic Regression (LR). According to model evaluation, it was found SVM and LR provides the excellent performance compared with the others.…”