Detecting Public Hate Sentiment Using Transformers

This research explores the application of deep learning techniques, specifically BERT and DistilBERT models, for detecting public hate sentiment in text data. Through a systematic analysis of the literature and a thorough understanding of the subject matter, we developed a groundbreaking method that leverages the power of these advanced models. Extensive experimentation and evaluation were conducted using a carefully curated dataset, employing techniques such as tokenization, padding, and truncating for preprocessing. The results demonstrate the efficacy of our approach, achieving high accuracy and precision in identifying and classifying hate sentiment. This research contributes to the field of natural language processing and provides valuable insights for effectively addressing and mitigating hate speech in online platforms.