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.
