Citation

Paul, Shaikat Chandra and Jahan, Busrat and Al Mamun, Abdullah Sarwar and Hossen, Md. Jakir (2024) An Approach to Detect Suicidal Bengali Posts from Social Media Using Machine Learning Algorithms. International Journal of Engineering Trends and Technology, 72 (5). pp. 43-50. ISSN 2231-5381

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Abstract

In this modern era, suicide is one of the critical issues. According to the WHO, more than seven million people die due to suicide every year. Suicide is also the second cause of unnatural death for persons between the ages of 15 and 29. Youth in nations like Bangladesh struggle with schoolwork, employment, relationships, drug use, and family issues, all of which are significant or minor contributors on the road to depression. In Bangladesh, people are uncomfortable discussing this ailment openly and frequently mistake this problem as madness. Many at-risk persons use social platforms to talk about their issues or get knowledge on related topics. This study aims to prevent suicide by identifying suicidal posts on social media. We collected suicidal-related data from Kaggle. (We use nine algorithms for three features). The prediction model achieved good performance. Stochastic Gradient Descent is the best model with the highest accuracy for unigram features, 87.23%. For bigram features, the Multinomial Naive Bayes is the best model with the highest accuracy, 88.69%. The best model with the highest accuracy for trigram features, 86.13%, is Stochastic Gradient Descent. This research demonstrates the chance that a machine-learning strategy can reduce the risk of suicide. Hopefully, this model will serve as a guide for lowering potential suicide risk in the future. The study concludes with a summary of several practical concerns that may be considered to improve model performance

Item Type:	Article
Uncontrolled Keywords:	Machine Learning Approaches, Social Media, Bengali Post
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Jul 2024 01:27
Last Modified:	02 Jul 2024 01:27
URII:	http://shdl.mmu.edu.my/id/eprint/12539

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