Generating Sub-emotions from Social Media Data Using NLP to Ascertain Mental Illness

It is predicted that mental illness will be one of the leading causes of death in 2030. Many people will not share their details of the illness detail with others, including family and friends. Also, many are unaware that their mental disorder is affecting their thinking and behavior. Early detection and medical intervention are necessary, otherwise it leads to severe problems. More than half of the world population, that is around 58.4% of the people use Social Media (SM) to express their thoughts and feelings. By fetching their timely thoughts and feelings expressed in social media we can analyze their emotions and sub-emotions. In this study, we developed a novel model to generate the sub-emotions of social media users from EmoLEX lexicon using the Affinity Propagation (AP) algorithm and word2vect conversion word2vec-google-news-300. The number of clusters and vocabulary obtained for ten emotions such as Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Trust, Positive and Negative is evaluated based on sub-emotions generated. By using word2vect conversions and AP algorithm it is found that the consistency of Mean words (µW) per cluster are equally distributed in each cluster with respect to all emotions on an average of 19, 20, 21 and 22. The obtained sub-emotions is used to mask the SM user post and it could be further used for detecting the mental illness of SM users.