A Machine Learning Approach to Identify the Preferred Representational System of a Person.

Whenever people think about something or engage in activities, internal mental processes will be engaged. These processes consist of sensory representations, such as visual, auditory, and kinesthetic, which are constantly being used, and they can have an impact on a person's performance. Each person has a preferred representational system they use most when speaking, learning, or communicating, and identifying it can explain a large part of their exhibited behaviours and characteristics. This paper proposes a machine learning-based automated approach to identify the preferred representational system of a person that is used unconsciously. A novel methodology has been used to create a specific labelled conversational dataset, four different machine learning models (support vector machine, logistic regression, random forest, and k-nearest neighbour) have been implemented, and the performance of these models has been evaluated and compared. The results show that the support vector machine model has the best performance for identifying a person's preferred representational system, as it has a better mean accuracy score compared to the other approaches after the performance of 10-fold cross-validation. The automated model proposed here can assist Neuro Linguistic Programming practitioners and psychologists to have a better understanding of their clients' behavioural patterns and the relevant cognitive processes. It can also be used by people and organisations in order to achieve their goals in personal development and management. The two main knowledge contributions in this paper are the creation of the first labelled dataset for representational systems, which is now publicly available, and the use of machine learning techniques for the first time to identify a person's preferred representational system in an automated way. [ABSTRACT FROM AUTHOR]