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By: Anshika Aneja, Apurva Garg, Aditya Nema, and Ruchi Jain.
Assistant Professor Department of Artificial Intelligence and Machine Learning Lakshmi Narain College of Technology and Science Bhopal, MP, India.
Student Department of Artificial Intelligence and Machine Learning Lakshmi Narain College of Technology and Science Bhopal, MP, India.
Student Department of Artificial Intelligence and Machine Learning Lakshmi Narain College of Technology and Science Bhopal, MP, India .
Department of Computer Science and Engineering Lakshmi Narain College of Technology and Science Bhopal, MP, India .
The exponential growth of social media platforms has led to an increase in user- generated content, enhancing global connectivity but also facilitating the spread of harmful language, including hate speech. Addressing this issue requires robust, Naïve Bayes, Decision Tree, K-Nearest Neighbours (KNN), Linear Regression, and Random Forest—were tested, with Linear Regression achieving the highest accuracy automated systems for detecting and mitigating offensive content. This paper presents a comprehensive analysis of methodologies involving machine learning (ML) and natural language processing (NLP) for hate speech detection, focusing on With a focus on Twitter as a data source because of its extremely dynamic and text-rich environment, we give a thorough review of approaches using machine learning (ML) and natural language processing (NLP) for the detection of hate speech in this paper. Naïve Bayes, Decision Tree, K-Nearest Neighbours (KNN), Linear Regression, and Random Forest classifiers were among the algorithms that were analyzed and assessed. The ability of each of these methods to detect objectionable material in sizable datasets was evaluated. Outperforming all of these models, Linear Regression outperformed the others with an accuracy rating of 94%. The results of this study demonstrate how promising natural language processing (NLP) is for improving the dependability of online content moderation systems. Automated systems can help create safer and more welcoming digital spaces by skillfully fusing statistical learning methods with linguistic analysis. However, there are still a number of restrictions, including how to deal with linguistic ambiguity, context-specific subtleties, and the ever-changing nature of online language. These difficulties highlight the need for more research that takes into account contextual embeddings, deep learning architectures, and hybrid strategies that combine supervised and unsupervised techniques.
Social media platforms, Hate speech, Automated systems, Machine learning (ML), Natural language processing (NLP), Twitter data, Linear Regression, Hate speech detection.
Citation:
Refrences:
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