Hybrid Deep Learning Framework for Personality Prediction in E-Recruitment

The Personality prediction is a vital task in the domain of psychology, human-computer interaction, and user behavior analysis, with applications ranging from tailored advertisements to mental health assessments. Traditional methods rely heavily on self-report questionnaires or psychological assessments, which can be time-consuming and subjective. To overcome these limitations, researchers are exploring automated and objective methods using deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Natural Language Processing (NLP) algorithms, which excel at capturing complex patterns in multimodal data. In this work, we propose a hybrid framework that combines CNN-based feature extraction and NLP algorithms for predicting personality traits using data such as facial expressions, speech patterns, and text analysis. The CNN model is leveraged due to its robust feature extraction capabilities, enabling it to learn intricate patterns directly from raw data inputs, which correlate with the Big Five personality traits. Additionally, we integrate a Support Vector Classifier (SVC) to classify personality traits based on the extracted features, offering improved prediction accuracy across diverse data sources.