Natural Language Processing : Use Cases

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Two dialogue blurbs containing characters

I first stumbled on this fancy term in Year 3 of my degree at UWI, and since then, I have been intrigued by the capabilities of Natural Language Processing, affectionately called NLP in the data community.

Now, you might be wondering, "What in the world is a Natural Language anyway?" 

A quick Google search will provide the following definition as the first result: "a language that has developed naturally in use (as contrasted with an artificial language or computer code)."

Essentially, natural languages are what humans speak, such as German and Arabic.

But guess what? Computers don't understand words and numbers like we do. 

Their whole world is built on a language of zeros and ones called binary.

However, they can understand computer languages such as Python and Java, which can then be translated into machine code.

The next question you may ask is, "How can a computer that speaks in zeros and ones process thousands of words in Spanish, French, or any other language?"

The answer is impressive mathematical equations—lots of them!

For example, NLP uses math to break down sentences into smaller parts, like words or phrases.

Now that you have a basic understanding of NLP here are four use cases:

Sentiment Analysis

Sentiment analysis, also known as opinion mining, allows businesses to gauge public sentiment towards their products, services, or brands.  This invaluable information helps companies understand customer feedback, monitor brand reputation, and make data-driven decisions.

Voice Assistants and Speech Recognition 

By employing techniques such as automatic speech recognition (ASR) and natural language understanding (NLU), voice assistants like Siri, Alexa, and Google Assistant can understand spoken language, respond to voice commands, and perform tasks such as setting reminders, making calls, or playing music. 

Chatbots

These intelligent virtual assistants can handle frequently asked questions, automate responses, and escalate complex issues to human agents when necessary, improving customer satisfaction and reducing response times.

Text Summarization 

 Automatic summarization techniques enable the extraction of key information from large volumes of text, producing concise and coherent summaries. 


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