However, due to the different nature of text in the world, machine learning is applied to NLP and it has gained a strong ground using SVM and Naïve Bayes. In past, NLP problems were solved using rule-based systems. Researchers are doing their best to generate interesting facts and figures from human language and implement those results in every field of life from educations to hospitals, industry to shopping malls, etc. NLP gained a lot of importance in recent years because of the researchers and processing powers of machines. Natural language processing is a scientific process to train a computer to understand and process human language. In the end, future directions are proposed. Heat maps are also generated for every model. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. A huge amount of data is generated on social media sites through social media users. NLP allows categorizing documents containing different texts. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks.

Finetune bert