• Anusha R
• thanu6226@gmail.com
• Arun kumar A
• Vinay R

Hate speech has become a significant problem today, with the potential to harm individuals and communities. One potential solution to this problem is to use machine learning algorithms to automatically detect and flag hate speech in text-based data. The process of hate speech detection using machine learning involves training a model on a dataset of labelled examples, where each example is labelled as hate speech or non-hate speech. Various features such as the use of certain words or phrases, grammar, and syntax are extracted from the text data, and the model learns to distinguish between hate speech and non-hate speech based on these features. The trained model can then be used to classify new text data as hate speech or non-hate speech. However, it is important to note that hate speech detection using machine learning is not perfect and can be affected by biases in the training data or in the algorithm itself. Ongoing research is focused on improving the accuracy and fairness of hate speech detection algorithms. Overall, hate speech detection using machine learning has the potential to be a valuable tool in the fight against hate speech, but careful attention must be paid to its limitations and biases.

Hate Speech, Machine Learning, Dataset, Text Analysis.

"Hate Speech Detection Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i460-i462, April-2024, Available :http://www.jetir.org/papers/JETIR2404861.pdf

"Hate Speech Detection Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi460-i462, April-2024, Available at : http://www.jetir.org/papers/JETIR2404861.pdf