Ensemble Learning Based Prediction for Cyber Harassment Observations on Tweets
N. Sreevidya *
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India.
Ashwini Hamsini
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India.
Rasala Vainateya
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India.
Chetla Arjun Naidu
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India.
*Author to whom correspondence should be addressed.
Abstract
Now a days social media plays crucial role in allowing individuals to express their views. Based on their views find the information of different keywords/statements like sadness, happiness, teasing, harassment and abuse. Online abuse, a novel form of pestering, have become increasingly predominant in online groups in modern civilization. Detecting harassment is indeed a significant challenge. Several studies provide information on cyber harassment, but none of them offer a solid remedy. Several studies provide information on cyber harassment, but none of them offer a solid remedy. Due to this reason, multiple models can be practiced to recognize and block harassment-related communications. We have utilized ensemble machine learning models to predict accurate results. The twitter dataset used for our research. We observe two models getting accuracy for RF+DT is 92% and SVM+LR is 93%. It is similar accuracy in individual models. So, there is no difference between Ensemble or individual model accuracy rate.
Keywords: Ensemble machine learning, prediction, cyber harassment, tweets
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References
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