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Detecting Self-harm Content and Behavior in Tweets with SVM and Ensemble Classifiers: A Comparative Study
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 43 |
| Year of Publication: 2024 |
| Authors: Divya Dewangan, Smita Selot, Sreejit Panicker |
10.5120/ijca2024924044
|
Divya Dewangan, Smita Selot, Sreejit Panicker . Detecting Self-harm Content and Behavior in Tweets with SVM and Ensemble Classifiers: A Comparative Study. International Journal of Computer Applications. 186, 43 ( Sep 2024), 21-32. DOI=10.5120/ijca2024924044
Abstract
Suicide is a mental state when the person loses the will to live, and it is a crucial issue nowadays. A person who is at suicidal risk needs early detection and medication. Researchers have exposed that many people relish posting their emotions and thoughts on social networking sites. In the past few years, Support Vector Machine (SVM) has been one of the most capable and vigorous classifiers in many fields of applications. Few areas where SVMs do not perform well have impelled the advancement of other applications, to enhance the strength of classifiers and parameters. Researchers from various disciplines have considered and explored the use of combination methodology. The idea of combination methodology is to build a prediction model by an ensemble of multiple methods. The main intention of this study is to develop and evaluate a model for suicidal content and behavior detection using the Support Vector Machine and Ensemble Classifiers such as Random Forest classifier, XGBoosting Classifier, and Stacking classifier. This paper summarized a brief introduction of SVM and Ensemble classifier. In this study, different Feature Extraction techniques, and their combinations have been used to train the models, and the accuracy and the performance of the models are analyzed. The findings highlight the substantial potential of the SVM and Ensemble classifier for accurately predicting suicidal content and behavior.
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