A multi-model attention based CNN-BiLSTM model for personality traits prediction based on user behavior on social media

The prediction of an individual's personality traits through the analysis of their online social media activities is an area of research that has gained considerable attention in the digital era. The statistical data derived from people's thoughts that are conveyed through their status updates on social media serves as an essential resource for understanding the various aspects of human behavior and personality. The present study is motivated by the various applications associated with personality prediction, such as targeted advertising, personalized entertainment, and customized recommendations. A multi-model attention-based convolutional neural network-bidirectional long short-term memory (CNN+BiLSTM) is proposed in the present work for personality traits prediction. The proposed model utilizes pre-trained language models, such as Global Vectors for Word Representation (GloVe) and Bidirectional Encoder Representations from Transformers (BERT), to create vector representations of words, which effectively captures word semantics. Additionally, network features such as size, betweenness, transitivity, and density are integrated with text features to enhance personality traits prediction. The results highlight the contextual understanding of the BERT model and emphasize the effectiveness of the proposed model, particularly the fusion of the attention layer with the CNN+BiLSTM architecture, which significantly improves information extraction and prediction capabilities. The integration of social network features strengthens the classifier's ability to predict personality traits, resulting in improved overall performance.