Recognition of hate speech using LSTM

Abstract

An important topic of research in natural language processing is hate speech identification, which aims to automatically identify and reduce offensive and harmful information on online networks. In-depth reviews and analyses of hate speech detection techniques are provided in this paper, with a focus on the application of Long Short-Term Memory (LSTM) networks. The fundamentals of hate speech recognition, the design and operation of LSTM, dataset preparation, model training, assessment measures, and obstacles encountered in the field are all covered in the study. This article aims to shed light on the state-of-the-art methods for hate speech recognition using LSTM by evaluating recent developments and continuing research. The urgency to develop intelligent, automated systems that can recognise and eliminate offensive content in real-time is the driving force behind hate speech recognition using LSTM. We can create safer and more welcoming online environments where individuals can openly express their opinions without worrying about harassment or discrimination by creating advanced algorithms. Additionally, in order for social media sites, news organisations, and other online communities to uphold their goodwill and user confidence, hate speech identification is essential. These platforms can promote healthier online relationships and improve user experiences by proactively identifying and deleting hate speech.