An Analysis of Depression Detection Techniques from Online Social Networks

Mental illness is caused by depression which may have a deep negative impact on individuals or on the society as a whole. It is a growing and severe problem which has the tendency to increase with time due to the extensive use of social networking websites such as Facebook, Twitter, Instagram etc. These social networking websites allow users to share images, videos, expressions and emotions. Depression is a form of mental illness. Patients suffering from depression have mood disorders such as low mood, high mood, lack of interest in things, etc. Machine learning techniques on text data and emojis have been applied to automatically classify a user into depressed and non-depressed. State-of-the-art classifiers have been used by the researcher to detect depressed individuals. Benchmark datasets are composed of text and emojis used in the social networking websites where classification is based on four factors Emotional Process, Temporal Process, Linguistic Style and combination of these three factors. In the proposed method, the emotional process is combined with their respective emojis to develop an automatic system for the detection of depressed patients. The features from the emotional process and emojis will be extracted and state-of-the-art classifiers have been proposed to be trained and evaluated using multiple classifiers using different combinations of part-of-speech tags.