10 Ways to Use Natural Language Processing (NLP) in Business

What did we do before automatically labeled emails, chatbots, predictive text, online translators, and even virtual assistants? 

We probably spent hours going through emails and sorting them, waited for instructions to reset passwords, sifted through foreign language dictionaries, and manually typed numerous questions into Google.

Many of the tools that make our lives easier today are possible thanks to Natural Language Processing (NLP) – a subfield of Artificial Intelligence that transforms text into something intelligible to machines. In short, NLP translates human language into numbers allowing computers to decipher even the subtlest of nuances in our complex language. 

See for yourself how NLP uncovers opinions, for example, by pasting text into this free online sentiment analyzer!

NLP tools are important for businesses that deal with large amounts of unstructured text, whether emails, social media conversations, online chats, survey responses, and many other forms of data. 

By bringing NLP into the workplace, companies can analyze data to find what’s relevant amidst the chaos, and gain valuable insights that help drive business decisions. 

So, how can NLP make your business smarter?

Top 10 NLP Applications

Natural Language Processing tools can help businesses analyze data and discover insights, automate time-consuming processes, and gain a competitive advantage.

Let’s take a look at 10 of the best uses of NLP in business: 

1. Sentiment Analysis 

Sentiment analysis uses NLP to recognize subtle information in text ‒ like emotions, opinions, and attitudes ‒ and determine how positive or negative they are. 

Using online sentiment analysis tools, you can: 

Try out this online sentiment analyzer to see how NLP sorts your text by emotions.

When you analyze sentiment in real-time, you can spot comments from disgruntled customers on the fly (and handle them before they escalate), gauge customer reactions to your latest marketing campaign or product launch, and get an overall sense of how customers feel about your company.

You can also do sentiment analysis periodically, and understand what customers like and dislike about specific aspects of your business ‒ maybe they love your new feature, but are disappointed about your customer service. Those insights can help you make smarter decisions, as they show you exactly what things to improve.

2. Chatbots

Chatbots, also known as virtual assistants, are designed to understand customer requests and deliver an appropriate response. 

Standard chatbots follow pre-defined rules, while AI-powered chatbots are able to learn from every interaction and uncover customer intent ‒ what customers intend to do next. Thanks to NLP, chatbots can differentiate between a customer wanting to check the status of their order and a customer that wants a refund.

Intelligent chatbots are increasingly present at the frontline of customer support, as they can help teams solve up to 80% of all routine queries and route more complex issues to human agents. Available 24/7, chatbots speed up response times, and relieve agents from repetitive and time-consuming queries.

3. Topic Classification

Topic classification is the task of identifying frequent themes or categories in unstructured text. 

Let’s say you want to analyze hundreds of open-ended responses to your recent NPS survey. Doing it manually would take you a lot of time and end up being too expensive. But what if you could train an NLP model to automatically tag your data in just seconds, using predefined categories and applying your own criteria? 

The result would be similar to this topic classifier for NPS survey responses, which automatically tags your data using categories like Customer Support, Features, Ease of Use, and Pricing. Give it a try and see how it performs!

4. Text Extraction

Text extraction automatically finds specific information in text. It uses NLP to recognize named entities ‒ like personal names, companies, locations, and more ‒, identify the main keywords within a text, and locate pre-defined features such as models and serial numbers for your products.

You can use a text extractor to sift through incoming support tickets and identify specific data, like company names, order numbers, and email addresses without needing to open and read each ticket. You might want to use it for collecting important data that needs entering into a CMS, for example. Paste text mentioning company names into this company extractor to see how it works!

Keyword extraction, on the other hand, gives you an overview of the content of a text, as this free KW extraction model shows. Combined with sentiment analysis or topic classification, keyword extraction can add an extra layer of insights, by answering questions like ‘which words appeared more frequently among those who felt negative towards your mobile app?’. 

5. Machine Translation

Machine translation (MT) is one of the main areas of research within NLP. Even though Facebooks’s translations have been declared superhuman, machine translation still faces the challenge of understanding context. 

Among other use cases, automated translation allows businesses to reach broader audiences with their products and helps them make sense of foreign documentation in a fast and cost-effective way. 

6. Text Summarization

Automatic summarization creates shorter versions of text, by reducing them to their most basic concepts. Its main goal is to simplify the process of going through vast amounts of data, such as scientific papers, news content, or legal documentation. 

There are two ways of using NLP to summarize data: extraction-based summarization ‒ which extracts keyphrases and creates a summary, without adding any extra information ‒ and abstraction-based summarization, that creates new phrases paraphrasing the original source. This second approach is more common and performs better.

7. Market Intelligence

Marketers can benefit from NLP to learn more about their customers and use those insights to create more effective strategies. 

Analyzing topics, sentiment, keywords, and intent in unstructured data can really boost your market research, shedding light on trends and business opportunities. You can also analyze data to identify customer pain points and to keep an eye on your competitors (by seeing what things are working well for them and which are not). 

8. Grammar Checking

Natural Language Processing plays a vital role in grammar checking software and auto-correct functions. Tools like Grammarly, for example, use NLP to help you improve your writing, by detecting grammar, spelling, or sentence structure errors. 

9. Intent Classification

Intent classification consists of identifying the goal or purpose that underlies a text. Apart from chatbots, intent detection can drive benefits in sales and customer support areas. 

By analyzing customer interactions like emails, chats, or social media posts, you can spot customers that are ready to purchase. The faster you can detect and classify those leads, the more chances you have of turning them into customers. Try this email classifier, and sort responses into categories like Interested, Not Interested, and Unsubscribe.

Finally, looking for customer intent in customer support tickets or social media posts can warn you of customers at risk of churn, allowing you to take action with a strategy to win them back.

10. Urgency Detection

NLP techniques can also help you detect urgency in text. You can train an urgency detection model using your own criteria, so it can recognize certain words and expressions that denote gravity or discontent. This can help you prioritize the most important requests and make sure they don’t get buried under a pile of unresolved tickets. 

Urgency detection helps you improve response times and efficiency, leading to a positive impact on customer satisfaction.

Final Note

Natural Language Processing makes it possible for machines to make sense of human language, enabling all kinds of exciting applications. 

NLP-powered tools help businesses process huge amounts of unstructured data, like customer support tickets, social media posts, survey responses, and more, and gain insights to support decision-making. Also, you can use these tools to automate time-consuming tasks, allowing machines to take over routine queries or speed up processes like ticket tagging and routing. 

SaaS tools are the most accessible way to get started with NLP. With an AI-platform like MonkeyLearn, you can start using pre-trained models right away, or build a customized NLP solution in just a few steps (no coding needed). 

Sign up to MonkeyLearn for free and discover all you can do with your data!

Rachel Wolff

Rachel Wolff

BA in journalism and French from Sheffield University. Interested in human-machine collaboration and Google's ever-changing algorithms.

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