Machine Learning Approach for Hate Speech Detection for Social Media

Authors: Dashamraj Tarachand Bante, Sakshi Tanaji Shinde, Tanuja Dadaji Khairnar, Namrata Uttam Panpatil

Country: India

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Abstract: The social network, which is such an important part of our lives, is plagued with online impersonation and fraudulent accounts. In online social networks, fake profiles are commonly used by intruders to carry out malicious activities such as harassing a person, identity theft, and privacy violations. As a result, determining whether an account is genuine or fraudulent is one of the most difficult tasks in OSN (Online Social Network) . Toxic online content has become a major issue in today’s world due to an exponential increase in the use of the internet by people of different cultures and educational backgrounds. Differentiating hate speech and offensive language is a key challenge in the automatic detection of toxic text content. This propose system is to automatically classify tweets on social media into two classes: hate speech and non-hate speech using the sentiment analysis technique. This proposed system build using NLTK and Textblob library to detect the sentiment (positive, negative, and neutral) of a given word. Textblob trained with millions of sentences using naïve bays algorithm.

Keywords: Hate Speech, Machine Learning, Encryption, Decision Tree Classifier, Textblob, NLTK, Natural Language Processing

Paper Id: 230205

Published On: 2023-06-01

Published In: Volume 11, Issue 3, May-June 2023

Cite This: Machine Learning Approach for Hate Speech Detection for Social Media - Dashamraj Tarachand Bante, Sakshi Tanaji Shinde, Tanuja Dadaji Khairnar, Namrata Uttam Panpatil - IJIRMPS Volume 11, Issue 3, May-June 2023.

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