prediction of hate speech classification using machine learning with nlp
Bhavani.B,Antony Sibiya Varghese V,Sevanthi.R,Jeevitha.A
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 27 April,2023 Pages:1828-1837
Abstract
The everyday lives of many people have been deeply affected by digital media. Hate speech is a narrative intended to distract or mislead the audience. Because of a number of factors, including the rise of online social networks in recent years, hate speech has become more common in the online world. Users of online social networks may easily be affected by this hateful speech. Hate speech has become a social problem, sometimes spreading more rapidly than the truth. Individuals are unable to identify all instances of hate speech. Thus, it is necessary to employ a machine learning algorithm to automatically detect hate speech. The development of machine learning models involves the utilization of algorithms that distinguish between speech that is considered hate speech, hurtful speech, or neither. Among the various algorithms, the Gradient Boosting Algorithm yields the highest level of accuracy. As a result, it has been chosen for use in this project's launch. The Kaggle dataset employed to classify hate speech comprises characteristics such as tweet count, hate speech, offensiveness, neutrality, classification, and tweet content.
Kewords
Machine learning, Online, Hate speech, Algorithm
Reference
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