Incorporating Emotion and Personality-Based Analysis in User-Centered Modelling

TL;DR

This paper explores how analyzing emotions and personality traits from user interactions in social networks can enhance user-centered models, leading to more intuitive system designs.

Contribution

It introduces a method for correlating user behavior with emotional and personality data using IBM Watson tools, advancing user modeling techniques.

Findings

Emotional and personality analysis provides valuable insights into user behavior.

Sentiment analysis correlates with user responses in social networks.

Modeling emotions improves understanding of user experience.

Abstract

Understanding complex user behaviour under various conditions, scenarios and journeys can be fundamental to the improvement of the user-experience for a given system. Predictive models of user reactions, responses -- and in particular, emotions -- can aid in the design of more intuitive and usable systems. Building on this theme, the preliminary research presented in this paper correlates events and interactions in an online social network against user behaviour, focusing on personality traits. Emotional context and tone is analysed and modelled based on varying types of sentiments that users express in their language using the IBM Watson Developer Cloud tools. The data collected in this study thus provides further evidence towards supporting the hypothesis that analysing and modelling emotions, sentiments and personality traits provides valuable insight into improving the user experience of complex social computer systems.