Understanding Natural Language Processing (NLP) for your Business

# Ways you can implement NLP algorithms in your own business

Heard of Natural Language Processing (NLP) but getting a hard time understanding what NLP is and how to use it in your own business. This article aims to provide you with a complete understanding of:

• What Natural Language Processing (NLP) is?
• Major Applications of NLP
• How you can use NLP in your own business to achieve your business goals?

Have you ever asked your queries to Amazon Alexa or Apple Siri or Microsoft Cortana or Google Assistant or Samsung Bixby? If yes, you’ve experienced Natural Language Processing (NLP) in action. You might also be surprised as to how they answer your query in a properly formulated and perfectly spoken human sentence in just a few seconds.

What Natural Language Processing (NLP) is?

From a simple Google search to writing a review about your favorite restaurant to tweeting in support of your favorite team to writing an article, you are generating a huge amount of text data on a regular basis which needs to be analyzed and understood. That’s where Natural Language Processing (NLP) comes into the picture.

Natural Language Processing (NLP) is used for analyzing, understanding, and deriving information from the text data in a smart and efficient manner.

The technology isn’t new, but we have seen rapid development in this field due to the huge amount of text data available and rapid advancement in computation power. Deep Learning Algorithms have significantly improved the accuracy of Natural Language Processing (NLP) tasks and are now the state-of-the-art algorithms for almost all the NLP tasks.

Big Tech Giants like Microsoft, Google, Amazon, Facebook, and small business as well are leveraging this technology to improve customer relationships and giving better products and services to their customer. The dream of “Customer First” policy can now be implemented easily and effectively, thanks to NLP. Let’s discuss some of the major challenges, NLP algorithms are able to solve.

Major Applications of Natural Language Processing (NLP)

1. Addressing Customer Needs and Pain — Sentiment Analysis: It’s super useful for gaining insights into customer opinions. Once you understand how the customers feel after analyzing their comments or reviews, you can identify what they like or dislike and build things like better products or more targeted marketing campaigns for them.

Sentiment Analysis is about determining the opinion or feeling of a piece of text.

2. Machine Translation: Algorithms are now able to convert source speech or text from one language (say English) to another language (say French). The rapid advancement of Deep Learning algorithms in the field of NLP has made this impossible task a reality. You can think of its application from education to communication to tourism.

Here’s a short video of Skype’s Machine Translation feature.

Just a fun fact: A deep network created by Oxford and Google DeepMind scientists, LipNet, reached a 93 percent success score in reading people’s lips, where an average human lip reader only succeeds 52 percent of the time. A group from the University of Washington used lip syncing to create a system that sets synthesized audio to existing video.

3. Spelling, Punctuations, word choice checker (Grammarly): Writing an article, or your project report, or an important document, be confident that your writing is polished, professional, and mistake-free. Grammarly does this for you. How? Yes, you are correct: Natural Language Processing (NLP).

4. ChatBots: Today the number of users of messaging apps like WhatsApp, Slack, Skype, and their analogs is skyrocketing, Facebook Messenger alone has more than 1.2 billion monthly users. With the spread of messengers, virtual ChatBots that imitate human conversations for solving various tasks are becoming increasingly in demand.

See this wonderful example of ChatBot (WoeBot) that provides Cognitive Behaviour Therapy (CBT). Let’s try to answer this big question: “What technology ChatBots like WoeBot used?”. Yes, you are correct, “Natural Language Processing (NLP)”.

This list of applications of Natural Language Processing (NLP) goes on and on. Basically, if you want to create some value on top of text or speech data (where your users will be providing your algorithm inputs in the form of speech and text), or you want to analyze your user's speech or text data, Natural Language Processing (NLP) is there for you. But still, the biggest question remains, “How can I use NLP in my own business to achieve my business goals?”

How you can use NLP in your own business to achieve your business goals?

This is a very important question. Based on my experience of 2+ years implementing NLP algorithms, I would suggest:

• First, identify whether you want to analyze your user's sentiment (like whether they like or dislike your product, which features they love the most, which features they are not satisfied with and want more improvements etc.) or you want to create some product or service on top of text or speech data (like Skype machine translation (service), Amazon Alexa (product) etc.).
• Once you know what product or service or algorithm you want to build, start simple.

There are multiple ways to build NLP algorithms, from using a predefined set of rules (advantage: quick to build, disadvantage: accuracy is low), to using machine learning algorithms (advantage: lots of open-source libraries available — easy to build, disadvantage: accuracy is good, but not excellent), to using state-of-the-art Deep Learning algorithms (advantage — offers excellent accuracy, disadvantage: hard to build and need a specialist to build).

• After the successful testing of your simple algorithm, deploy it as this will put your product or service in the market without many efforts and more importantly, quickly.
• As your business and resources grow, start improving your NLP algorithms using Machine Learning or state-of-the-art Deep Learning algorithms, which will put your business in the front delivering best value products and services that your users will love using.
• There are multiple easy to use APIs available too for many NLP task by big tech giants like Google, Microsft. This is something that you can explore too.

Conclusion

Businesses are using Natural Language Processing (NLP) algorithms to better understand customer intent through sentiment analysis, extract insight from unstructured data and alleviate customer frustration.

With the huge amount of data being generated each day, the ability to fully analyze text from every source will be a differentiator.

Medium Profile: You can follow me on Medium as well