• Mumthaz Samad
• Salma.N
• Aisha Saju
• Bini wilson
• Sabira A S

Using the Random Forest Classifier algorithm, we propose a complete method that combines text analysis with image classification to counteract toxic speech in online comments and texts. The classification of speech as toxic encompasses terms like "obscene," "toxic," "severe toxic," "threat," "insult," and "identity hate." This poses a serious obstacle to the promotion of constructive online dialogue. Putting these categories together under the heading of "Toxic" speech material will help us build a strong algorithm that can recognize and naturally remove harmful content from social media networks that are accessible online. Apart from textual analysis, we acknowledge the significance of picture content in identifying harmful conduct. Our methodology aims to offer a more complete method for recognizing harmful speech across many modalities by integrating picture classification techniques. Our objective is to create a high-accuracy classifier that can identify harmful content in both text and photos by utilizing machine learning algorithms. Our research intends to help create safer and more inclusive online communities by tackling toxic behavior in both textual and visual modes. We aim to create a more positive online community atmosphere and healthier online interactions by reducing the dissemination of harmful speech.

Natural language processing (NLP),hate speech detection,toxic speech,online communities,machine learning,classification techniques

"Hate speech classification using machine learning algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.k472-k474, April-2024, Available :http://www.jetir.org/papers/JETIR2404A63.pdf

"Hate speech classification 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. ppk472-k474, April-2024, Available at : http://www.jetir.org/papers/JETIR2404A63.pdf