Natural Language Processing or NLP is the branch of artificial intelligence that aims to facilitate communication between humans and machines by using natural human language as an interactive medium.

It combines elements of data science, computing, and linguistics to develop systems and applications capable of interpreting, understanding, and acting upon natural language input in the form of spoken or written text.

Much of this activity requires coding and the construction of dedicated software architecture, so natural language processing programming exists as a specific field in the development arena.

Semantic and syntax analysis forms a significant part of natural language processing, as does the development of NLP algorithms based on machine learning principles. Some of the core computing languages used in natural language processing have a data science and statistical analysis focus.

MATLAB, a fourth-generation programming language, and platform often used in representing and working with matrices. A high-performance technical computing language, MATLAB typically performs the mathematical computation and algorithm development underlying natural language processing operations.

The programming language R uses statistical methods and graphs to play a role in investigating big data, supporting NLP research and performing computationally intense learning analytics. A considerable number of natural language processing algorithms have been developed in R, making the language an ideal tool for NLP modeling and prototypes.

NLP Programming with Python
A lot of the coding activity in the natural language processing realm takes place in Python, an interpreted programming language with a syntax that often reads like standard English. With an ecosystem that actively supports the implementation of Artificial Intelligence (AI) and Machine Learning (ML) systems, Python also offers various libraries and other resources that facilitate NLP programming.

Chief among these is the Natural Language ToolKit (NLTK), the most popular library for natural language processing. It includes functions and data sets to support the most common techniques used in NLP, such as calculating how many times a particular word or token appears in a given piece of text (the FreqDist word frequency distribution class), extracting and tokenizing text data from HTML or XML files (the Beautiful Soup library), and performing sentiment analysis on blocks of text to determine whether the opinions expressed there are positive, negative, or neutral (VADER, or the Valence Aware Dictionary and sEntiment Reasoner).

NLP Programming with Java

The most popular programming language for Android smartphones, Java is platform-independent and has an established history of facilitating conversational interfaces. Though natural language processing with Java can be a complex and challenging affair, successful NLP implementations in Java enable users to explore how to automatically organize text data using full-text search, clustering, tagging, and information extraction.It’s possible to implement Natural Language Processing in a JavaScript project without struggling with integrating an external API (Application Programming Interface). You can do this with Cerebrum.js, an open-source package designed to perform advanced Machine Learning operations such as Natural Language Processing. Cerebrum.js enables programmers to process data within a JavaScript project, easing NLP implementation and reducing the cost and privacy risk of paying for an external API or transferring data to external servers.