Deep Learning Model to Analyze Psychological Effects on Gamers

The rapid growth of the gaming industry has sparked interest in understanding the psychological effects of games on gamers. This paper explores using deep learning models to analyze the psychological effects experienced by gamers. With video games' increasing popularity and influence, understanding their impact on human psychology is paramount. This research aims to develop a deep learning model that can detect the state of human facial expressions and classify various psychological states based on gameplay data. The proposed deep learning model utilizes Convolutional neural networks to extract meaningful features and patterns from gameplay data. The model is trained on a diverse dataset of gameplay recordings, capturing a wide range of game genres, player demographics, and psychological experiences. The evaluation and validation of the model are performed using metrics, including accuracy, precision, recall, and F1 score. The findings reveal that the deep learning model is effective in detecting and classifying psychological states experienced by gamers. The model successfully captures changes in human facial expressions, such as sadness, happiness, fear, anger, surprise, and disgust, providing valuable insights into the psychological impact of gaming. The results highlight the importance of considering individual differences and contextual factors when examining the influence of games on human psychology. We can advance our knowledge and promote healthy and meaningful gaming experiences by continuing to investigate this complex relationship.