Recent Techniques for Sentiment Analysis on Medical Data to Predict Depression: A Review

Abstract

With the advent of technology, most medical organizations have developed medical platforms where a large group of patients computes the textual data. Depression is a common type of mental disorder that has a relative impact on society. People use social media platforms and share their emotions, ideas, and thoughts with others. Hence, an automated health monitoring scheme is essential to monitor the health status of the patients. Through the monitoring of the social media platforms, the medical sentiments of the persons can be analyzed by the user comments. This paper presents the standard domains for analyzing the medical sentiments emphasized on depression. The process of sentimental analysis is described with its steps. The procedure comprises the collection of medical data from social sites, preprocessing, extracting the features and applying a classifier to classify the data. There are several existing methods of sentimental analysis based on the traditional machine learning methods, semi-supervised statistical methods, deep learning algorithms like long short-term memory classification model and many more. In addition, the notion of medical sentiment analysis has been described. In this paper, we discussed the existing methods for examining medical emotions using social media platforms such as Facebook and Twitter. The process of medical sentiment analysis is depicted using the approaches provided, which are then compared using performance metrics to identify its applications and challenges.