Natural Language Processing (PDF) is the study of making computers understand how humans naturally speak, write, and communicate. Armed with this understanding, computers can perform sentiment analysis and other text analyses on a huge scale to provide you with meaningful data.
Traditionally, communicating with a computer would require giving it very precise, unambiguous, and highly structured instructions, written in dedicated programing languages, like Java, C++, and Ruby.
But humans don’t speak to each other like they speak to computers.
In the real world, human communication conveys messages in ways that, while structured with grammar, can be imprecise, ambiguous, and will contain regional slangs and idioms. NLP aims to bridge the gap between the differences in communication.
Modern techniques and approaches are based on machine learning, where artificial intelligence examines patterns within data to draw conclusions on how natural human languages work. Applying these conclusions, machines can perform nlp tasks including entity recognition, sentiment analysis, text parsing, speech recognition, part-of-speech tagging, summarization, etc.
For example, consider the following sentence: “The amazing Cloud delivers data to me ASAP.”
With current NLP methods, machines will break down the piece of text into its grammatical elements (“amazing” = adjective, “Cloud” = noun, “delivers” = verb, etc.), understand that the “Cloud” is referencing “Cloud Computing”, and recognize that “ASAP” is a common acronym for “As Soon As Possible”. Using this information, NLP provides the foundation for further text analytics, sentiment analysis, and other linguistic analyses.