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Understanding Text Classification In Natural Language Processing — A Beginners’ Guide
Natural language processing is a complex field with many applications. In this article, we will break down the basics of text classification in natural language processing and provide a step-by-step guide to understanding it. Understand how NLP can be used to create powerful algorithms that can automatically classify documents, aid in search optimisation, or help identify topics or sentiment in user-generated content. Dive into text classification and the various algorithms used to achieve it!
Introduction to Text Classification
As the world becomes more and more digital, our ability to process and understand large amounts of data is becoming increasingly important. Natural language processing (NLP) is one of the most exciting and powerful fields in computer science that deals with this challenge.
Text classification is one of the most common tasks in NLP. It is the process of assigning a label or category to a given piece of text. For example, we can classify emails as spam or not spam, tweets as positive or negative, and articles as relevant or not relevant to a given topic.
The goal of this guide is to introduce you to the basics of text classification so that you can start building your own models.
What is Natural Language Processing (NLP)?
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
NLP research is interdisciplinary, involving work from many areas including psychology, cognitive science, mathematics, statistics, sociology, anthropology and others. In recent years there has been an increase in commercial interest in NLP technologies as companies strive to develop more sophisticated ways of analysing and making use of the burgeoning amounts of digital textual data being generated every day.
