Abstract
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.
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Acknowledgment
This work reported herein was fully supported by the Fundamental Research Grant Scheme (FRGS) under reference number (Ref: FRGS/1/2018/SS09/UiTM/02/2). In addition, the authors would like to thank the Ministry of Higher Education (MOHE), Malaysia, and Universiti Teknologi MARA (UiTM), Malaysia, for supporting the research.
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Nor, N.M., Rahman, N.A., Yaakub, M.R., Zukarnain, Z.A. (2022). Sentiment Analysis on Depression Detection: A Review. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_48
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