Comparison of Hate Speech Identification in Kannada Language Using ML and DL Models

The problem at hand is to create a discrimination system specifically for Indian languages, with an emphasis on Automatic Speech Recognition (ASR) implementations. Hate speech poses a serious challenge to online websites and social media, as well as causing harm, such as spreading hate, inciting violence, and promoting inequality. Macro skills are discriminatory, there is an urgent need for a similar system for regional languages as the country has many different languages and unique cultures. Therefore, this paper intends to gauge the overall performance of 4 characteristic engineering strategies and 4 gadget learning algorithms to examine their overall performance on a publicly-to-be-had dataset with two distinct classes. The experimental consequences confirmed that the bigram capabilities when used with the help vector machine set of rules great carried out with 88% accuracy in ML and 91% of accuracy in DL. This observation has practical implications and can be used as a basis for detecting automated hate speech messages. Moreover, the output of different affinity could be utilized as country-of-artwork strategies to compare destiny research for existing computerized text classification techniques.