Personality Traits Prediction Using DISC

This research introduces a novel method of optimizing the process of cultural fit assessment in corporate recruitment through the combination of natural language processing (NLP) methods and semi-supervised self-training models. Using an unlabeled dataset drawn from tweets, we used preprocessing techniques and word2vec embeddings to determine semantic relationships in the text. K-means clustering was used to find the optimal number of clusters (k=4). In cooperation with psychology professionals, we marked 10% of the dataset based on the DISC model of psychology to facilitate model training. A mixed model integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) was created to properly leverage labeled and unlabeled data via semi-supervised learning. This study will simplify cultural fit evaluation during recruitment, making it possible to quickly and accurately evaluate candidates.