Research Area:  Machine Learning
The statistics presented by the World Health Organization inform that 90% of the suicides can be attributed to mental illnesses in high-income countries. Besides, previous studies concluded that people with mental illnesses tend to reveal their mental condition on social media, as a way of relief. Thus, the main objective of this work is the analysis of the messages that a user posts online, sequentially through a time period, and detect as soon as possible if this user is at risk of depression. This paper is a preliminary attempt to minimize measures that penalize the delay in detecting positive cases. Our experiments underline the importance of an exhaustive sentiment analysis and a combination of learning algorithms to detect early symptoms of depression.
Author(s) Name:  Victor Leiva & Ana Freire
Conferrence name:  Internet Science
Publisher name:  Springer
Paper Link:   https://link.springer.com/chapter/10.1007/978-3-319-70284-1_34