Sentiment analysis and affective computing for depression monitoring

Abstract

Depression is one of the most common and disabling mental disorders that has a relevant impact on society. Semiautomatic and/or automatic health monitoring systems could be crucial and important to improve depression detection and follow-up. Sentiment Analysis refers to the use of natural language processing and text mining methodologies aiming to identify opinion or sentiment. Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. Sentiment Analysis and Affective Computing methodologies could provide effective tools and systems for an objective assessment and monitoring of psychological disorders and, in particular, of depression. In this paper, the application of sentiment analysis and affective computing methodologies to depression detection and monitoring are presented and discussed. Moreover, a preliminary design of an integrated multimodal system for depression monitoring, that includes sentiment analysis and affective computing techniques, is proposed. Specifically, the paper outlines the main issues and challenges relative to the design of such a system.