Classification of Proactive Personality: Text Mining Based on Weibo Text and Short-Answer Questions Text

Abstract

This study focused on the topic of predicting ''proactive personality''. With 901 participants selected by cluster sampling method, targeted short-answer questions text and participants' social media post text (Weibo) were obtained while participants' labels of proactive personality were evaluated by experts. In order to make classification, five machine learning algorithms included Support Vector Machine (SVM), XGBoost, K-Nearest-Neighbors (KNN), Naive Bayes (NB) and Logistic Regression (LR) were deployed. Seven different indicators, which include 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 cross-validation were also used to make the comprehensive evaluation of models. With participants' Weibo text and short-answer questions text, we proposed a new approach to classify individuals' proactive personality based on text mining technology. The results showed that short-answer questions + Weibo text datasets had the best performance, followed by short-answer questions text datasets, while the outcome of Weibo text datasets were the worst. However, it is noteworthy that Weibo text has the highest average score on the SPE, which indicated that Weibo text played an important role in identifying individuals with low proactive personality. With Weibo text, SEN was also improved compared with only applying short-answer questions text. In addition, among all three datasets, the indicator SPE is always higher than SEN, indicating this text classification approach was more competent for identifying college students with low proactive personality. As for algorithms, Support Vector Machine and Logistic Regression showed steadier performance compared with other algorithms.INDEX TERMS Machine learning, proactive personality, text mining.

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

“…The differences from our previous study are below. Firstly, in our previous work, participants' labels of proactive personality were evaluated by experts [24]. While our new work in this article, the difference from our previous work is that after the participants were dispensed proactive personality questionnaires and were asked to give answers (mostly rely on self-reports) to four choices, new approaches were adopted to classify individuals' proactive personality based on text mining technology.…”

“…While our new work in this article, the difference from our previous work is that after the participants were dispensed proactive personality questionnaires and were asked to give answers (mostly rely on self-reports) to four choices, new approaches were adopted to classify individuals' proactive personality based on text mining technology. Secondly, only F-test was adopted for feature selection in our previous work since labels of high and low proactive personality categories are corresponded to certain features that have a large difference, so in this way features can be distinguished with F-test [24]. While in our new work, besides F-test, 2 test was also used for feature selection.…”

Research on e-Commerce Distribution Optimization of Rice Agricultural Products Based on Consumer Satisfaction

“…When all ants complete a cycle, the pheromone on the path needs to be updated. The specific formula is shown in (14) and (15).…”

“…Text mining can directly obtain the unknown value information hidden inside a text and can extract political, commercial, cultural, and other information from complex text through methods such as viewpoint mining and sentiment analysis [12][13][14]. Compared with questionnaires and interviews, the information obtained by text mining is more authentic and objective and is not affected by the subjective will of the sender [15].…”

Survei Tentang Analisis Sentimen Pada Media Sosial

“…Penelitian [20] membahas tentang memprediksi kepreibadian proaktif. Denggan 901 perserta dipilih dengan metode cluster sampling, teks pertanyaan jawaban singkat ditargetkan dan sosial peserta teks postingan media ( Weibo ) diperoleh sementara laberl kepribadian proaktif perserta dievaluasi oleh para ahli.…”