Bigram

Trigram

Voice activity detection

Feature (linguistics)

Support vector machine

Geodesy

World Wide Web

Machine learning

Philosophy

Benchmark (surveying)

Artificial intelligence

Geography

Set (abstract data type)

Programming language

Natural language processing

Computer science

Linguistics

Social media

Feature extraction

Speech recognition

Speech processing

Social media and knowledge sharing have had a positive impact on humanity. However, this has also led to a number of issues, such as the dissemination and dissemination of hate speech. This new problem of hate speech on social media has been addressed by recent studies that utilized a number of feature engineering techniques and machine learning algorithms. It&#x0027;s not clear if there is a study that compares different methods for generating features and machine learning algorithms in order to determine which one is better for a standard publicly available dataset. With the support vector machine technique, the testing findings showed that bigram features performed best with 79 percent overall accuracy when utilized with the bigram feature set. Detecting automated hate speech messages can be made easier with the findings of our investigation. It will also be used as a benchmark for future research into existing automatic text classification algorithms, based on the results of the various comparisons. The use of natural language processing to classify text and hate speech are all examples of machine learning

Machine Learning based Automatic Hate Speech Recognition System