Relationship classifier and stress analyzer in the mobile messaging application network through text mining

Emotions are at the peculiar element for thoughtfulness and perception ourselves and others, and the computerized annotation and recognition of emotion could enhance our knowledge and understanding with technologies. Nowadays, mobile messaging application as well as other social applications has become a dominant part of an individual´s social interaction and relationships. The aura around these applications where people share their expression, feelings and perception are emotionally contented. The proposed framework for differentiating emotional interactions messaging application, and then to distinguish relationships as friends, family, and professional colleagues. The aim is to mine the emotional content of textual expression in the mobile messaging system. The framework also includes the generation of special dictionaries for endless short form languages, for emotags, interjections and for frequently used foreign language words.This Mobile message emotion extraction model consist of two modules, one for relationship classifier and Stress analyzer and reliever, which includes different levels for mining the emotional data, i.e. text gathering, database tables, text processing and emotional text mining then through an unsupervised technique which uses the kmeans clustering algorithm to determine individuality of texts and predict relationships.