What is NLP?

Companies are flooded with unstructured data, making it impossible to analyze and process all that data without the help of Natural Language Processing (NLP).

Read on to discover natural language processing, how NLP can make businesses more effective, and popular natural language processing techniques and examples.

Natural Language Processing (NLP), a sub-branch of artificial intelligence (AI), is a text analysis technique. It helps machines process and understand human language so they can perform repetitive tasks automatically. Natural Language Processing includes applications such as language translation, summarization, auto-correction and auto-completion, and social media monitoring (If you want to learn more about natural language processing applications, you can check out the article "Use Cases and Benefits of Natural Language Processing".

For example, discuss sentiment analysis, which uses natural language processing to detect sentiment in text. This classification duty is one of the most popular issues in NLP and is often used by companies to find brand sentiment on social media automatically. Analyzing these customer reviews can help brands identify pressing customer issues they need to respond to or monitor overall customer satisfaction.

Why Is Natural Language Processing Important?

Natural language processing is so critical for companies because it can be used to analyze large volumes of text data such as social media conversations, customer support data, online reviews, news and more.

All this consumer data contains valuable insights, and NLP can quickly help companies discover what those insights are.

NLP does so by helping machines make sense of human language faster, more accurately, and more consistently than a human.

NLP tools process data instantly and apply similar techniques to all your data, so you don't have to doubt the accuracy and consistency of the results obtained.

Just as other NLP-enabled tools like Kimola Cognitive understand what a text is about and even measure things like sentiment, businesses can begin to prioritize and organize their data to fit their needs.

🌮Also see: Definition of Natural Language Processing (NLP) and Its Applications

🌮Also see: How to analyze texts by using NLP at Kimola Cognitive