Emotion Predictor Using Social Media Text and Graphology

Social media is a growing source of data and Social Media activities such as WhatsApp group communications have opened up a myriad of data and information exchange - especially amongst the modern student community. On an average, they tend to express their feelings and opinions far more freely amongst their net cronies than anywhere else. In this paper, the target is to evaluate the emotional state of students within such a group, by extracting hidden nuances through usage of words revealed by text analytic measures. The study is further augmented with records of personal statistics. Some of these, such as age, educational background and social status, help to categorize the participants for ease of comparative analysis at a later stage. The rest, gleaned through collecting signatures and handwritten scripts of participants, support the discipline of graphology, for delving into the depths of the writer's character traits. Overall, the primary goal is to propose a Case Based Reasoner (CBR) to hold all the details of each student as individual cases with attached sentiment polarity scores reflecting the personal positivity or negativity. The Case Base needs to undergo incremental learning, until the model is judged to have gained sufficient experience to start predicting polarities on its own.