Depression Prediction Using LSTM and SVM

As there has been an increase in the number of mental illness cases, there is a need to curb this problem. Due to the complexity of traditional techniques based on clinical diagnosis, there should be an automated system for the detection and prevention of illness and hence there comes the need to develop a depression prediction system. The data is collected from twitter, followed by preprocessing and cleaning which includes removal of stop words, URL and HTML tags, expanding abbreviations etc. Following this, the process of feature extraction will be used to extract word count, pronouns, negations and other features from the comments made by the users following selection. Long Short Term Memory and Support Vector Machine classifiers are applied to obtain the results. Further, the tokenized words are embedded into a vector and passed to the encoder in and then to perform depression prediction, a classification layer is added on the top of the transformer output. Similarly the tokenized input is embedded into a vector of SVM, which is mapped to a class label, followed by classification into depressed and non-depressed classes. Both methods are compared for depression analysis. This helps in providing an early detection of depression in people. Social network and microblogging sites such as Twitter are widespread amongst all generations nowadays where people connect and share their feelings, emotions, pursuits etc. Depression, one of the most common mental disorder, is an acute state of sadness where person loses interest in all activities.