Unsupervised Feature Learning for Text Pattern Analysis with Emotional Data Collection: A Novel System for Big Data Analytics

Abstract

When it comes to our everyday life, emotions have a critical role to play. It goes without saying that it is critical in the context of mobile–computer interaction. In social and mobile communication, it is vital to understand the influence of emotions on the way people interact with one another and with the material they access. This lesson tried to investigate the relationship between the expressive state of mind and the efficacy of the human–mobile interaction while accessing a variety of different sorts of material over the course of the learning. In addition, the difficulty of the feeling of many individuals is taken into account in this research. Human hardness is an important factor in determining a person's personality characteristics, and the material that they can access will alter depending on how they engage with a mobile device. It analyses the link between the human-mobile interaction and the person's mental toughness in order to provide excellent suggestion material in the appropriate manner. In this study, an explicit feedback selection method is used to gather information on the emotional state of the mind of the participants. It has also been shown that the emotional state of a person's mind influences the human-mobile connection, with persons with varying levels of hardness accessing a variety of various sorts of material. It is hoped that this research will assist content producers in identifying engaging material that will encourage mobile users to promote good content by studying their personality features.