Word embeddings play a significant role in shaping the information sphere and can aid in making consequential inferences about individuals. Job interviews, university admissions, essay scores, content moderation, and many more decision-making processes that we might not be aware of increasingly depend on these NLP models. NLP is used to understand the structure and meaning of human language by analyzing different aspects like syntax, semantics, pragmatics, and morphology. Then, computer science transforms this linguistic knowledge into rule-based, machine learning algorithms that can solve specific problems and perform desired tasks. As with the processing task of the natural language machine learning and deep learning algorithms have played a very important role in almost all of the applications of natural language processing. NLP is used to analyze text, allowing machines to understand how human\u2019s speak. NLP is commonly used for text mining, machine translation, and automated question answering.<\/p>\n