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All Journal Bulletin of Electrical Engineering and Informatics
Esha, Israt Jahan
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Electrical & Electronics Engineering

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Depression detection in social media comments data using machine learning algorithms Vasha, Zannatun Nayem; Sharma, Bidyut; Esha, Israt Jahan; Al Nahian, Jabir; Polin, Johora Akter
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4182

Abstract

Depression is the next level of negative emotions. When a person is in a sad mood or going through a difficult situation and it is not leaving him and giving him pain continuously and he is unable to bear it anymore, that situation is called depression. The last stage of depression occurs in suicide. According to the World Health Organization (WHO), Currently, 4.4% of people in the world are currently suffering from depression. In 2021, fourteen thousand people committed suicide all over the world and the rating of suicide is increasing day by day. So, our study is to find depressed people by their comments, posts, or texts on social media. We collected almost 10,000 data from Facebook posts, comments, and YouTube comments. Data mining and machine learning (ML) algorithms make our work easier and play a big role in easily detecting a person’s emotions. We applied six classifiers to predict depression non-depression and found the best accuracy on a support vector machine (SVM).
Co-Authors Al Nahian, Jabir Polin, Johora Akter Sharma, Bidyut Vasha, Zannatun Nayem