A study of hate speech detection in online forums using NLP

Abstract

Hate speech detection has emerged as a critical topic of research in online platforms, to mitigate the negative consequences of discriminatory language and promote a safer digital environment. Using advances in Natural Language Processing (NLP), academics developed a variety of ways for automatically recognizing and categorizing hate speech in text data. This paper provides a detailed assessment of hate speech detection systems based on AI methods, highlighting significant methodologies, problems, and achievements in the field. We will use CNN, RNN and LSTM models to get the best accuracy.Each of these models has its unique strengths and is suited for different types of tasks within the field of deep learning.We begin by looking at the fundamental methodology used in hate speech identification, such as feature engineering, supervised machine learning algorithms, and language analysis tools. Feature engineering is critical for collecting the semantic and historical context required for recognizing hate speech, whereas supervised machine learning algorithms enable model training to distinguish between hate speech vs free speech instances. Furthermore, linguistic analysis techniques such as sentiment analysis and syntactic parsing help to extract significant aspects from text data.