Study of Depression Analysis using Machine Learning Techniques
Devakunchari Ramalingam1, Vaibhav Sharma2, Priyanka Zar3
1Devakunchari Ramalingam, Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
2Vaibhav Sharma, UG Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
3Priyanka Zar, UG Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
Manuscript received on 15 May 2019 | Revised Manuscript received on 22 May 2019 | Manuscript Published on 10 July 2019 | PP: 187-191 | Volume-8 Issue-7C2 May 2019 | Retrieval Number: G10440587C219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Depression is a major health issue that imparts major impact on the stability of mind. With the extension of various social media platforms, an expansion of number of different platforms enabled people to interact and share their experiences. These provided a large dataset for identification of common traits among depressed people and identify them using various machine learning algorithms. The limit to which we can identify the depressed traits of the person is necessary to determine the level of depression. The classification plays a major role in determining the kind of help a depressed person needs and also, the person with suicidal thoughts need to be identified and helped according to his condition. This paper provides the survey about the use of machine learning techniques in the analysis of depression with their research issues.
Keywords: Machine Learning, Depression, Mental Stability, Social Media, Suicidal.
Scope of the Article: Machine Learning