Sentiment Analysis on Depression Detection: A Review

• Published in: Lecture Notes in Networks and Systems
• Main Author: Nor N.M.; Rahman N.A.; Yaakub M.R.; Zukarnain Z.A.
• Format: Conference paper
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135061081&doi=10.1007%2f978-3-031-10464-0_48&partnerID=40&md5=5f20c30b31e557aad250a88897005149

Depression has become a public health issue. The high prevalence rate worsens all scopes of life irrespective of age and gender, affects psychological functioning, and results in loss of productivity. Early detection is crucial for expanding individuals’ lifespan and more effective mental health interventions. Social networks that expose personal sharing and feelings have enabled the automatic identification of specific mental conditions, particularly depression. This review aims to explore the sentiment analysis to the psychology area for detecting depressed users from the datasets originating from social media. Sentiment analysis involves five research tasks, but this study investigates the sentiment analysis that focuses on emotion detection in the text data. This paper surveys existing work on the most common classification approach in machine learning to classify linguistic, behavioral, and emotional features and presents a comparative study of different approaches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.