Scopus Indexed Publications

Paper Details


Title
Depression detection in social media comments data using machine learning algorithms
Author
Zannatun Nayem Vasha, Bidyut Sharma, Eisrat Jahan Esha, Jabir Al Nahian, Johora Akter Polin,
Email
zannatun15-12939@diu.edu.bd
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). 

Keywords
Data mining; Depression detection; Facebook; Machine learning; Social media
Journal or Conference Name
Bulletin of Electrical Engineering and Informatics
Publication Year
2023
Indexing
scopus