8 Most Important Natural Language Processing (NLP) Applications in 2023

Konstantin Sadekov
January 3rd, 2023

NLP in customer service: Titan holds a chatbot, connecting to clients

Why are NLP applications important for your business?

The amount of information and the number of interactions online is growing exponentially, ranging from social media content to the never-ending stream of customer interactions. The need to access and synthesize text in real-time or as soon as it is posted becomes increasingly important. Businesses can’t keep up with this never-ending stream of information by simply hiring more customer service representatives. Instead, they must incorporate natural language processing (NLP) solutions into their operational workflow.

  • NLP is a field operating at the intersection of artificial intelligence, computer science, and linguistics.
  • NLP applications are capable of solving a wide set of text, syntax, semantics, and speech problems.
  • NLP applications decipher text by simulating the human ability to understand the language.

In this article you will:

  • learn about 8 most important NLP applications
  • understand how you can use them in your everyday business operations

Let’s dive in together to understand the 8 most relevant NLP applications and explore how can they help you become more profitable.

1. Text classification

Text classification is the core task in natural language processing. The goal of text classification is to read the text and assign one label from a fixed set of categories to it based on its content and detected keywords. For example a typical use case could be document classification. Despite its simplicity, text classification is often the first step in many natural language processing pipelines. The reason is that many businesses can’t extract valuable insights from unstructured text data, such as emails, news, customer interactions, or investor outlook before the data is categorized in a certain way. And this is where text classifiers shine.

The text classifier is a supervised learning algorithm that quickly and accurately sorts the text into categories or topics on which the algorithm was trained on. Let’s illustrate this with an example. Imagine that you want to sort all news headlines or Twitter feeds into 3 categories: 1) technology, 2) sports, and 3) fashion. In the training phase, you feed this news together with its correct labels into the algorithm. In the prediction phase, you apply this algorithm to the new unseen Twitter feeds and the algorithm will predict its topic.

raw text data


Text classifiers can be applied to a variety of textual datasets, be they emails, tweets, news headlines, or news articles. Overall, text classifiers can predict topic, sentiment, intent, emotion, or perform any kind of classification you want the algorithm to be trained on. Below, we also explore sentiment analysis in more detail.

2. Sentiment analysis

Sentiment analysis is a subfield of text classification. Its goal is to process the text to classify the author’s sentiment towards something. This sentiment can be classified into categories, such as positive, neutral, or negative, or into broader response groups, such as anger, disappointment, fear, or satisfaction. Popular use cases of sentiment analysis include advertising, brand management, finance, or trading.

example of sentiment analysis at different levels


For instance, many banks, cryptocurrency exchange platforms, and portfolio management companies use market sentiment analysis to predict the trend in stock or cryptocurrency prices. Market sentiment is an aggregate measure of market opinions, moods, and outlooks that investors voice through social media or microblogging websites. Portfolio management companies agree that market sentiment is highest just before the market reaches high and lowest just before the market reaches rock-bottom. As a result, market sentiment analysis creates attractive investment opportunities for investors before there is any news to explain this market movement.

3. Machine translation

Machine translation systems are extremely helpful for any business with global operations or global customer reach. Linguistic studies show that out of the world’s 7.5 billion inhabitants, only 5% speak English as their first language and only additional 15% have some degree of English comprehension. Therefore, if you are relying only on English or your company’s native language, you are missing out on a significant fraction of your potential customer or client base. Machine translation breaks down this barrier by quickly and accurately translating catalogs, reports, technical manuals, or customer interactions.

The cutting-edge machine translation algorithms also include localization which addresses a variety of semantics and contextual factors, such as local idioms, cultural references, measurement units, date formats, and page sizes. An example of localization is translating from US English to French where imperial measurements (feet, inches, etc) are converted to metric units (meter, centimeters). Similarly, dates will be localized as well. For instance, 1/12/2020 indicates January 12th in the US but will be translated to 12.1.2020 in French to preserve the correct meaning. The cutting-edge technology behind machine translation systems is deep learning networks, which translate the words in the source text based on the localization and context of that word used in the sentence.

4. Bias and fake news detection

Digital misinformation and fake news have been declared as the major technological and socio-political risk by the World Economic ForumFake news is created with malicious intent to misinform readers, generate unnecessary polarizations among opposing groups, and drive traffic to the news with clickbait headlines or politically biased content. Recent examples include climate change propaganda or the 2016 presidential elections in the US where around 100 million Facebook accounts were targeted by fake social messages backed by Cambridge Analytica. As an illustration, the Facebook AI team recently released a natural language processing model called RoBERTa, which was later transformed to detect whether the news is real or fake based on the localization and semantic context of tokens it contains.

5. Named entity recognition

Named entity recognition is a natural language processing application that identifies named entities from a text and assigns them into pre-defined categories. Named entities can be names of people, universities they attended, companies they worked at, dates, and quantities. Entity identification is extremely valuable for businesses that require fast and scalable analysis of long and unstructured text documents. One prevalent real-world use case is an analysis of applicants’ resumes by HR departments. Your HR department probably receives thousands of applications for a single position and spends countless hours wading through candidates’ profiles to find the perfect match. Each resume is formatted differently, yet contains the same type of information, such as the candidate’s name, universities they graduated from, or previous companies. Deploying entity identification enables your recruitment teams to immediately extract critical information, classify it into your pre-defined categories, and create a uniform snapshot that lets you evaluate multiple candidates instantly.

6. Predictive text modeling

Predictive text modeling or next word prediction is an extremely useful NLP application deployed in messaging applications, such as WhatsApp, email, Google Search, and chatbots. The algorithm learns to predict the next word in a sentence by understanding the localized context from that sentence. As an illustration, when I type how to learn in the Google Search box, I already see some predictions. I see that certain next words are predicted given the existing words how to learn. The autocomplete or next word prediction saves a lot of time for your users and helps them express their needs by understanding their patterns of writing and background.

google search

7. Chatbots

A chatbot is a scalable and efficient solution to automate your customer service, recruiting, and marketing workflow. The chatbot is a starting point for a conversation with your website visitors. It finds out what the visitors are searching for before they drop off. One of the key advantages of chatbots is that they replace long waiting times when a customer has an inquiry with real-time, precise answers 24/7. This can significantly boost your conversion rates, help you nurture clients, and generate revenue. It also prevents important clients from dropping off or slipping through due to missing their message or not responding quickly enough.

Chatbots are relatively easy to implement with very little input from you, such as pre-defined responses. In turn, they significantly cut down your costs by automating your workflow and escalating to the right people on your team with more advanced queries. It is a win-win scenario. But there are different types of chatbots you can implement into your business depending on your use case, one of which is the AI chatbot.

8. Recommender systems

Content-based recommender systems are another popular NLP application that provides delightful user experiences and creates sales funnels. Many large retailers, such as Zalando, Amazon, eBay, and Netflix, leverage recommender systems. Content-based recommender systems rely on two approaches: item-item similarity and user-user similarity. We are going to focus on item-item similarity because it requires less specific knowledge about a particular visitor.

As an illustration, all clothing products sold by fashion giant Zalando are labelled with hundreds of attributes, such as color, body shape suitability, price, length, occasion, season, and gender. Whenever a visitor buys something from Zalando, let’s say grey jeans, other articles that match those jeans’ attributes will be automatically recommended.

The NLP algorithm relies on similarity metrics, such as Euclidean distance or correlation similarity, to tell us how closely two articles with all their attributes are correlated. Zalando reports that outfit recommendations drive 40% larger purchases. All in all, recommender systems help customers effortlessly explore similar outfits while driving incremental revenue.

Next steps

We’ve gone through the 8 most important NLP applications and outlined the ways they can help you automate your workflow. Check the natural language processing use cases, as this will help to identify relevant examples for your industry. But please don’t stop here. Check also how to qualify and choose the right NLP company for your needs to avoid costly mistakes.

P.S. Our team has extensive experience with designing all of these systems and is available to work with you on the best solution for your specific needs. Reach out today to get started on this exciting journey!

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