Hate Speech Detection and Classification Using NLP
Syed Shahidh Ilhan, Soubraylu Sivakumar, Jagadesh Nagaraj, S. Ramesh, N. Sreeram, R. Rajalakshmi · 2024
Detected hate speech instances are appropriately classified, rebuilding the intricate landscape of disparaging language in an increasingly digitized society. We present an exhaustive analysis of the techniques, gravitating towards their efficiency, precision, and capability to adapt to the evolving dynamics of language manifestation online. The study uncovers fascinating insights into the relative strengths, weaknesses, limitations, and potential areas of improvement of Bi-LSTM and the Bi-LSTM with GRU in the realm of hate speech detection and classification. The results of Bi-LSTM with GRU has obtained an accuracy of 95% with an improvement of 1% over Bi-LSTM model. Serving as a cornerstone in the burgeoning NLP research, our findings provide promising direction and robust groundwork for future improvements and attempts in the continuous drive towards safer, more respectful digital communication platforms.