20 Natural Language Processing Examples For Businesses

If this is the first time you’re hearing about Natural Language Processing (also known as NLP), this basically deals with using machine learning to derive meaning from human languages.

Now, this might seem like pretty innovative, cutting edge technology, but the truth is that NLP is something that’s been part of our lives for years now. In fact, consumers from across the globe interact with NLP on a daily basis, without even realizing it.

Want to learn more about NLP, and its many uses? In this blog post, we share 20 natural language processing examples across a wide range of industries.

PS: We’ve previously published a post on natural language processing examples as well, but this is a new and improved (and more substantial!) version of that previous post. Read on to find out more!

To expand on our earlier definition, NLP is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. To learn more about NLP, watch this video.

Now that you’ve got a better understanding of NLP, check out these 20 natural language processing examples that showcase how versatile NLP is.

Top on our list of natural language processing examples is none other than… social media monitoring! 

If you’ve ever used a social media monitoring tool such as Hootsuite or Buffer, these are basically built using NLP technology. These tools help you to monitor social media channels for mentions of your brand, and alert you when consumers are talking about your brand. 

As many marketers and business owners will know, having a negative review go viral on social media can destroy a brand’s reputation overnight. Bearing this in mind, it’s important for companies to engage in social media monitoring or listening, and make sure that they address any potential crises immediately.

Next on our list of natural language processing examples is sentiment analysis, which is basically a smaller subset of social media monitoring. 

While the latter refers to monitoring the social media landscape and listening in on conversations as a whole, the former deals specifically with identifying opinions and determining whether the author of the post holds a positive, negative, or neutral opinion towards a brand.

Again, NLP comes into the picture here. Basically, using NLP, sentiment analysis tools pick out emotionally-charged words that are used to describe a brand and/or a customer’s experience with a brand.

For instance, if a post contains plenty of positive language such as “amazing”, “fantastic”, “wonderful”, then the tool might conclude that the overall sentiment is positive. 

With sentiment analysis, companies can gauge how receptive their customers are to a particular product or service, or even to a recent change that they’ve implemented (eg a chance in their returns policy, support policy, etc).

Text analysis can be broken into several sub-categories, including morphological, grammatical, syntactic and semantic analyses.

By analysing text and extracting different types of key elements (such as topics, people, dates, locations, companies), companies can better organize their data, and from there, identify useful patterns and insights.

For instance, eCommerce companies can conduct text analysis of their product reviews in order to find out what customers like or dislike about their products, and how customers are using their products. To do this, these companies can use NLP-equipped tools such as Wonderflow’s Wonderboard. 

Apart from analysing their product reviews, companies can also analyse their survey results in order to come up with actionable insights. Again, NLP helps these companies to make sense of all their raw data, and generate useful insights and takeaways.

Of course, companies who are conducting small-scale surveys might choose to manually analyse their data and come up with recommendations.

That said, if you’re surveying your entire database of 10,000 customers, then it isn’t feasible to sit down and sift through all the results yourself. Here, automating the process using an NLP-equipped tool makes more sense.

Think spam isn’t a huge problem? Think again. According to statistics, spam accounts for 45% of all emails sent, and about 14.5 billion spam emails are sent every single day.

Now, looking at the above statistics, you might be wondering why you don’t get more spam. Well, that’s because we’ve got excellent spam filters that flag dodgy emails as spam, and prevent them from reaching our primary inboxes.

How do these spam filters work? Among other factors (deliverability, email domains, etc), these filters use NLP technology to analyse email subject lines and their body content. 

From here, it’s fairly easy for them to ascertain what’s spam and what’s not — emails that contain plenty of capitalized text and words such as “free”, “promotion”, “buy now”, etc, have a high chance of being spam. 

Running in the same vein, there’s also email classification, which you’ll be familiar with if you’re a Gmail user.

Basically, when you look at your Gmail inbox, you’ll see that your emails are categorized in three tabs — Primary, Social, and Promotions. All your personal emails go into Primary, your notifications from social media platforms go into Social, and newsletters from companies that you sign up to hear from land in Promotions.

Here, Gmail uses NLP to identify and evaluate the content within each email, so that it can categorize them accurately. The system isn’t 100% foolproof, though, which is why you might find some newsletters (especially ones that contain more text than images) getting filtered to your Primary tab. 

With autocomplete, Google predicts what you’re interested in searching for based on the initial few characters or words that you enter. 

To suggest relevant keywords for you, Google relies on a treasure trove of data that catalogs what other consumers are looking to find when entering specific search terms. To make sense of that data and understand the subtleties between different search terms, the company uses NLP. 

If you often text on the go, or you have fat thumbs that make it hard for you to hit the right keys on your keyboard, you’ll appreciate the beauty of autocorrect. 

Like autocomplete, autocorrect relies on NLP technology. Here, NLP identifies the closest possible term to your misspelling, and automatically changes your misspelled term to the accurate one instead.

Autocomplete and autocorrect aside, there’s also spell check, which both students and working professionals rely heavily on. 

Imagining having to submit an important essay without being able to look through your work, or sending an email to the CEO of your company, without spell check.

Sure, you could look through your text manually, but there’s no denying that spell check is more effective at flagging out misspellings or grammatical errors.

Spell check aside, other writing tools such as Grammarly, ProWritingAid and WhiteSmoke also utilize NLP to correct users’ spelling and grammatical mistakes.

When you click on the search function on a website and search for a certain keyword, the website returns the relevant results, so that you can find exactly what you’re looking for. It might sound simple, but behind the scenes, it’s a whole different story.

For example, say you’re browsing an eCommerce store selling school supplies for children, and the store has tens of thousands of listings. If you want to look for “canvas backpacks”, and you start off your search query with the term “canvas”, how does this store know to highlight its backpacks at the top of its listings, and feed you with the most relevant listing?

Again, NLP saves the day here. With NLP, a store can pick up on context and add contextually relevant synonyms to search results. This helps the store accurately predict exactly what their customers are searching for, and highlight the relevant listings. 

Many forums and question and answer sites such as Quora use duplicate detection technology to keep their site functioning at their best.

For example, imagine you search for “Are fresh blueberries healthier than frozen?” on Quora, and you see a whole list of similar questions that have been previously asked, including:

• Are fresh blueberries healthy?
• Are frozen blueberries healthy?
• Which is healthier, fresh or frozen blueberries?
• Is it better to consume fresh instead of frozen blueberries?
• Is buying fresh blueberries better?

Obviously, if you have to click on each question and review the answers for that question, and make your way down the entire list, this takes a lot of time and energy. If all the questions have been collated into a single question or thread, on the other hand, this makes it easier for you to review the answers and come to a conclusion.

Because of this, Quora uses NLP to reduce the instances of duplicate questions, as much as possible. Once a user finishes typing their question, Quora then analyses it to determine if it’s linguistically similar to the other questions on its site, and serves up a list of similar questions that users can just refer to (instead of posting a new question).

If you’re travelling someplace where English (or your native language) isn’t commonly spoken or understood, then you’ll definitely want to install a translation app on your phone.

By far the most popular tool is Google Translate, which is used by 500 million people every day to understand more than 100 world languages. Google Translate relies on NLP to understand the phrases or terms that its users are trying to translate, and the same goes for all the other alternative translation apps out there. 

With live chat, there’s a chat agent on the end of the “line”, helping you answer your enquiry. But ever wondered how chatbots understand what it is that you’re asking them, and answer your questions?

This boils down to NLP. Using NLP and machine learning, chatbots can decipher consumers’ questions, recommend them products, book meetings and appointments for them, and more.

Standard-issue chatbots are a dime a dozen, but these days, companies are increasingly building upgraded chatbots to supercharge their customer service efforts.

Take Mastercard’s chatbot that was launched back in 2016, for instance. This chatbot functions like a virtual assistant, and is “almost as good as having a bank teller in your pocket”. 

Among other things, the chatbot can give users a high-level overview of their spending habits, highlight the special benefits and promotions that they’re entitled to, and more. As with regular chatbots, these upgraded bots also utilize NLP technology to understand users’ queries.

Ever heard of self-serve knowledge bases? These knowledge bases are basically a portal or online library of information, including FAQs, troubleshooting guides, and more. 

By building knowledge bases, companies are empowering their customers to resolve their own problems 24/7, instead of contacting a company’s support department, and having to wait to hear back from them.

Because knowledge bases often contain thousands of documents, it’s in the company’s best interest to help their customers identify the right materials quickly. To do this, companies can link their chatbots up with their knowledge base, and configure their bots to send customers links to help docs relevant to their queries.

Smart home devices such as Alexa and Google Home are becoming increasingly popular, especially among younger consumers. (Case in point? 58% of millennials say they currently own smart home devices with voice control capabilities.)

These smart home devices are great for multi-tasking — if you want to put on some music, but you’re tied up with cooking dinner, for example, you can simply instruct Google Home to turn on your favorite playlist, and it’ll do so immediately. Here, your smart home device uses NLP to recognize your voice commands and carry out the right action. 

PS: When you issue a voice command to your smartphone assistant (eg Google Assistant or Siri), NLP is also working behind-the-scenes, and allowing your assistant to understand your instructions.

Imagine a tool that doesn’t just analyze or identify trends from text data, but goes one step further, and formulates insights about a product or service for a user to read. Yep, this technology actually exists… and you can use it by signing up for Wonderflow’s Wonderboard. 

How does this work? In a nutshell, Wonderboard draws on the text data that you’ve fed it to compose sentences by simulating human speech. Again, it uses NLG and machine learning to do this. 

If someone has a question such as, “What is the most negative topic for this product and is it relevant?” Wonderboard can offer an answer by drawing upon the data accumulated earlier for analysis.

Algorithmic trading (also known as automated trading or black-box trading) basically involves uses an algorithm to place a trade. 

There are two different categories of algorithmic trading. At the basic level, consumers can define instructions (pertaining to time, price, and volume) that the program can use to place a trade. For example, if you state that you want to buy 5 lots of Amazon stocks when the share price drops to US$1,600, the program can execute your instructions.

On a higher level, algorithmic trading can also involve the use of robo-advisors to make recommendations on optimizing a portfolio. Here, the program reviews countless data that affect financial markets (including companies’ financial results, news on mergers and acquisitions, etc), and come up with recommendations on what stocks an investor should buy or sell. NLP is crucial in helping these programs make sense of the data and information.

In most clinics, patients would give their symptoms to the nurse or counter staff, and this person would make notes to share with the physician. That said, clinics and medical companies are now using NLP to streamline patient information, and automate the process of understanding a patient’s condition.

To do this, companies can utilize 98point6’s automated assistant, which is an NLP-powered tool that patients share their information with. Prior to sitting down with their physician, a patient would simply text their health history and condition to the app, and the app would then streamline the relevant information and present it to the physician. 

Wrapping up our list of natural language processing examples is aircraft maintenance, which is pretty different from anything we’ve talked about thus far.

How does NLP help in aircraft maintenance? First and foremost, by utilizing NLP tools, mechanics can more easily synthesize information from immensely wordy aircraft manuals. On top of that, aircraft companies can also use NLP to analyze its reports submitted by pilots or other aircraft personnel, and improve their processes and systems from there.