Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing

Neuro Linguistic Programming (NLP) is a methodology used for recognition of human behavioural patterns and modification of the behaviour. A significant part of this process is influenced by the theory of representational systems which equates to the five main senses. The preferred representational system of an individual can explain a large part of exhibited behaviours and characteristics. There are different methods to recognise the representational systems, one of which is to investigate the sensory based words in the used language during the conversation. However, there are difficulties during this process since there is not a single reference method used for identification of representational systems and existing ones are subject to human interpretations. Some human errors like lack of experience, personal judgment, different levels of skill and personal mistakes may also affect the accuracy and reliability of the existing methods. This research aims to apply a new approach that is to automate the identification process in order to remove human errors thereby increasing the accuracy and precision. Natural Language Processing has been used for automating this process and an intelligent software has been developed able to identify the preferred representational system with increased accuracy and reliability. This software has been tested and compared to human identification of representational systems. The results of the software are similar to a NLP practitioner and the software responds more accurately than a human practitioner in various parts of the process. This novel methodology will assist the NLP practitioners to obtain an improved understanding of their clients’ behavioural patterns and the associated cognitive and emotional processes

Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®

Neuro Linguistic Programming (NLP) is a collection of techniques for personality development. Meta programmes, which are habitual ways of inputting, sorting and filtering the information found in the world around us, are a vital factor in NLP. Differences in meta programmes result in significant differences in behaviour from one person to another. Personality types can be recognized through utilizing and analysing meta programmes. There are different methods to predict personality types based on meta programmes. The Myers–Briggs Type Indicator® (MBTI) is currently considered as one of the most popular and reliable methods. In this study, a new machine learning method has been developed for personality type prediction based on the MBTI. The performance of the new methodology presented in this study has been compared to other existing methods and the results show better accuracy and reliability. The results of this study can assist NLP practitioners and psychologists in regards to identification of personality types and associated cognitive processes

Natural Language Processing approach to NLP Meta model automation

Neuro Linguistic Programming (NLP) is one of the most utilised approaches for personality development and Meta model is one of the most important techniques in this process. Usually, when one speaks about a problem or a situation, the words that one chooses will delete, distort or generalize portions of their experience. Meta model, which is a set of specific questions or language patterns, can be used to understand and recover the information hidden behind the words used. This technique can be adopted to understand other people’s problems or enable them to understand their own issues better. Applying the Meta Model, however, requires a great level of skill and experience for correct identification of deletion, distortion and generalization. Using the appropriate recovery questions is challenging for NLP practitioners and Psychologists. Moreover, the efficiency and accuracy of existing methods on the Meta model can potentially be hindered by human errors such as personal judgment or lack of experience and skill. This research aims to automate the process of using the Meta Model in conversation in order to eliminate human errors, thereby increasing the efficiency and accuracy of this method. An intelligent software has been developed using Natural Language Processing, with the ability to apply the Meta model techniques during conversation with its user. Comparisons of this software with performance of an established NLP practitioner have shown increased accuracy in identification of the deletion and generalization processes. Recovery of information has also been more efficient in the software in comparison to an NLP practitioner