Emotional intelligence is both characteristic of personality and intellectual capacity, which a person inherits from the genetic material of its parents and evolves – develops throughout lifetime. It refers to information processing capacity arising from the emotions and their utility to guide action in situations that require activation of the cognitive system. The purpose of the present research work is the application of Machine Learning and Data Mining methods for the evaluation of emotional IQ in a sample of students and social network consumers (age 18-26 years). Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. The data were collected by completion of the self-report questionnaire Trait Emotional Intelligence (TEIQue) and used for the application of data mining methods. Then the collected data were selected for analysis, with relevant transformations in order to have a suitable form for the implementation of the respective machine learning algorithms included in the software package R. Furthermore, the parameters of the corresponding set of algorithms were determined depending on the case of application to produce inference rules. Some of the algorithms implemented according to specific research questions that were applied, were the classification algorithms (ID3 and J48) for the production of decision trees, regarding the four more general factors (welfare, self-control, emotionality and sociability) and in overall emotional intelligence. The results obtained, after weighing and criteria basis, present consumers’ rates, which in turn analyze the degree of emotional intelligence.