Application of Various Machine Learning Techniques in Sentiment Analysis for Depression Detection
Soundariya R S1, Nivaashini M2, Tharsanee R M3, Thangaraj P4
1Soundariya R S , Department of Computer Science & Engg his/her department, Bannari Amman Institute of Technology, Erode, India.
2Nivaashini M, Department of Computer Science & Engg his/her department, Bannari Amman Institute of Technology, Erode, India.
3Tharsanee R M, Department of Computer Science & Engg his/her department, Bannari Amman Institute of Technology, Erode, India.
4Thangaraj P, Department of Computer Science & Engg his/her department, Bannari Amman Institute of Technology, Erode, India.
Manuscript received on 02 October 2019 | Revised Manuscript received on 13 October 2019 | Manuscript Published on 29 June 2020 | PP: 292-296 | Volume-8 Issue-10S2 August 2019 | Retrieval Number: J105208810S19/2019©BEIESP | DOI: 10.35940/ijitee.J1052.08810S19
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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 the world’s fourth leading disease and will be in the second in 2020 according to the statistics of World Health Organization. Depression affects many people irrespective of their age, geographic location, demographic or social position and more commonly affects females than males. Depression is a mental disorder which can impair many facets of human life. Though not easily detected it has intense and wide-ranging impressions. Although many researchers explored numerous techniques in predicting depression, still there is no improvement and the generations are facing higher rate of depression. It is believed that the depression detection algorithms can be more accurate and their performance can be better if they rely on artificial intelligence. On considering these factors, it is planned to perform a survey on the application of various machine learning techniques that have been used in the domain of sentimental analysis for depression detection.
Keywords: Depression detection, Machine Learning, Sentiment Analysis, SVM, Decision Trees, Depression rating mechanisms, BDI, HAMD.
Scope of the Article: Machine Learning