Predictive Emojis for User Cognitive Response to Dynamic Behaviour of Webpages Using Pupil and Click Reinforcement

The consequences of a poor user experience are based on the same principles that affect the user experience of any website. These principles span the gap between, for instance, a business webpage and a brick-motor company; the simple reason for this is whether online or in-store the cause or the cognitive processes of the users are similar in certain ways. The main steps towards improving the user experience of websites are through the understanding of the psychology of the users. This can be demonstrated using graphical-based emotion emojis. To understand the correlates and user affective response and how this changes by the dynamic behaviour of webpages, cognitive predictive markers such as bubble emojis are used to model users’ emotions to the objects they view on different webpages presented to them. The psychology and website user experience are framed at first impressions, through processing fluency, loading race, the limits of attention, and age of anxiety. This paper demonstrates an effective way of understanding user cognition through eye movement from the webcam and mouse clicking movement from finger touch during the interaction. User-generated data are collected and used as biomakers in form of emojis that relay the users’ emotion to the contents they view online. A computational test was carried out to determine the reliability of the proposed model (predictive dynamic control) with a standard artificial neural network and the results show compatibility as compared to state-of-the-art models. During process optimisation and flow, the bubbles seem to appear at undefined area of interest (AOI) of the visual webpages, and the selected URLs of some of these pages appear at random listings as defaults; these are some of the drawbacks of the proposed model. The originality of the project is its ability to automatically call up webpage in real-time with great processing speed and utilise the analytic control to predict users’ mood from previous search generated data and at the same time control synchronisation link between pupil changes and click reinforcement kernel.