Hybrid Deep Learning Framework for Personality Trait Analysis from Text

A hybrid deep learning method for categorizing personality traits from textual input is proposed in this paper. Contextual information can be extracted from text by the model through the integration of Bidirectional Long Short-Term Memory (BiLSTM) networks for sequence learning and Convolutional Neural Networks (CNNs) for feature extraction. To improve semantic understanding, the method makes use of pre-trained word embeddings, such as Word2Vec and GloVe. Datasets with text samples tagged with Big Five personality model personality attributes are used to train the model. In comparison to conventional machine learning techniques, the suggested method performs better and achieves high accuracy in personality trait classification from text, making it a useful tool for psychological evaluations and HR management.