Detecting the magnitude of depression in Twitter users using sentiment analysis

• Published in: International journal of electrical and computer engineering (Malacca, Malacca) Vol. 9; no. 4; p. 3247
• Main Authors: Stephen, Jini Jojo, P., Prabu
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.08.2019
• Subjects: Communication; Correlation analysis; Data mining; Digital media; Mathematical analysis; Mental depression; Mental disorders; Mental health; Sentiment analysis; Social networks
• ISSN: 2088-8708; 2088-8708
• DOI: 10.11591/ijece.v9i4.pp3247-3255

Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data.