NLP Use Cases: Dissecting Its Impact Through Examples

Giants like OpenAI and Google have been taking advantage of NLP use cases to make their AI solutions smarter. The question is- are there more industries exploring its potential? Let’s have a look!

There’s this buzzword taking over the tech market- NLP. Everyone’s talking about it: NLP this… NLP that…. But the catch is- most industries haven’t scratched the surface of the transformative potential hidden in NLP use cases. 

How do we know that? Well, according to a Statista report, the AI market was expected to reach a massive size of $243.70 billion in 2025. Yet, the NLP market shows strong indications to remain capped at $35.11 billion by the end of the year. That’s just 14.41% of the total AI landscape. It clearly means that NLP adoption is still a far-fetched idea for many businesses.

To emphasize well that NLP is a core component of the whole AI ecosystem, we will introduce its use cases in the blog further. But first, let’s answer the question that is turning aspiring data scientists into night owls:

What is NLP in AI?

Natural Language Processing (NLP) is the way to teach a machine to understand the patterns of human language. A subfield of Computer Science, NLP is how you can help a computer understand human emotions, meanings of expressions, and more. 

Furthermore, picture an endless stream of unstructured data: images, emails, survey responses, etc. Somewhere in all that chaos lies the secret to unlocking key digital marketing trends, boosting market growth, and even saving lives. How do you curate this data and structure it to align with the growth of your business? By using NLP tools and services.

Consider NLP a way to bridge the gap between humans and computers and make usability more accessible. Tech maestros are coming up with new NLP use cases every now and then by focusing on strategies to build chatbots, Artificial General Intelligence (AGI), etc. 

To simplify more, NLP acts as a sophisticated filter, transforming unstructured data into trends and actionable insights. The best way to understand natural language processing examples is to dive deeper into their use cases. Let’s have a look!

Examples of Natural Language Processing (NLP)

Applications of natural language processing are diverse in nature, and so are its examples. From customer interactions to the entertainment industry, NLP has a role to play everywhere. 

Let’s dive deeper to understand all of this better.

1. Sentiment Analysis in Customer Feedback

Companies already use NLP to understand customers’ opinions of their brands or products. They collect data using reviews, surveys, social media interactions, and website comments to understand the prospects of future reputation and sales. By blending the customer experience strategy with AI-powered digital transformation, marketers can use sentiment analysis to understand customer feedback better.

This image here, shared by Towards Data Science, shows how NLP-fueled AI keeps a record of texts used to make any opinion. The image is about clothes. Based on this data, Data Scientists can frame Sentiment polarity scores. If the score falls to zero in the majority, that’s bad news. 

Case Study: 

Marriott, a giant in the hospitality industry, is planning to invest in AI throughout the year to leave an impact in three key areas: accelerated content generation, elevated customer experience, and augmented intelligence for Marriott’s associates.

Leveraging NLP-powered sentiment analysis, Marriott aims to streamline sentiment data gathering using multiple platforms. The goal is to offer more personalized services and leave an impact that is much deeper. The total amount that the hospitality industry giant is planning to spend on NLP use cases can go as high as $1.2 billion.

2. Chatbots and Virtual Assistants

AI-powered Chatbots remain one of the prominent examples of natural language processing in the modern tech ecosystem. From advanced chatbots like GPT to virtual assistants like Alexa, NLP powers an extensive range of applications. The tech empowers such technologies to understand responses more efficiently and provide more human-like responses in conversations.

Case Study:

GPT, one of the giants among the generative AIs, can be the perfect example of natural language processing use cases in the industry. The chatbot doesn’t only understand your inputs well, it also frames outputs in the most humanized way possible. 

As GPT upgraded, each version, whether GPT 3 (trained on 175 billion parameters) or GPT 4 (trained on 1.8 trillion parameters), got more sophisticated and thoughtful with its results. Using NLP, GPT can generate anything from stories to poems.

3. Language Translation and Localization

Language translation is getting smoother thanks to the integration of natural language generation technologies. Translation apps, thanks to NLP, are delivering flawless translations and the touch of localization in results. Localization ensures the translation also delivers cruxes of the local culture in NLP use cases, making it more impactful on the local audience.

Case Study:

Google Translate has been continuously evolving since its inception; it can be seen from the quality of translations it has delivered. Now, by implementing Neural Machine Translation (NMT), which is a branch of Natural Language Processing (NLP), Google Translate delivers more humanized responses if needed.

The perk of using NMT is its ability to scan the entire text and structure it logically based on the context. As a result, over 55% of translation errors have been reduced. 

4. Fraud Detection in Financial Services

Among the benefits of NLP is automating fraud detection. The functionality includes observing patterns in texts and detecting anomalies. To make conclusions, communication records, transaction descriptions, and other such unstructured text datasets are observed. NLP-powered AI systems can identify possible frauds based on changes in patterns by using the data gathered.

Case Study:

A study by Springer Nature Link talks about the potential of user-privacy-safe NLP solutions to automate fraud detection activity. It introduces FraudNLP, an online fraud detection made anonymized for public usage. The study argues the potential of FraudNLP can even surpass state-of-the-art solutions when it comes to improving fraud detection accuracy. 

It emphasizes the fact that using textual data, transaction descriptions, and more such resources can be the key to smoothly distinguishing between legitimate and fraudulent activities. 

5. Content Recommendation Services

NLP use cases are now often observed in content recommendation systems as well. While the integration of AI in personalization isn’t a new thing, NLP makes personalization much smoother and more interesting. The integration of NLP helps identify user feedback and user interaction behavior, as well as deliver customized recommendations to users. 

Case Study:

Spotify’s ability to stay ahead in using technologies makes it one of the best music apps. It leveraged AI for a while to deliver personalized experiences. However, to make this personalization more sophisticated, it focused on taking advantage of improving the quality of NLP as well. Spotify continuously keeps an eye on the web to understand which music and artists are being talked about on the internet. 

NLP helps it understand the context better. Then, Spotify groups users together according to their tastes. Furthermore, it uses one user as a reference to recommend songs to another user, songs that they haven’t listened to before. 

6. Medical Record Analysis

Unstructured medical records can be hard to go through, but traditional AIs aren’t capable of summarizing them without losing some context or critical data. Thankfully, NLP use cases such as contextual summarization made the process much smoother. 

Integration of NLP in medical data analysis can speed up the whole process. Now, even 500 pages can be converted into contextual summaries for a quick go-through, thanks to the existence of smarter algorithms and understanding of contexts.

Case Study: 

Maruti Techlabs designed a machine learning model to make its data management process much more efficient. Its client, UKHealth, has a staff of doctors generating thousands of letters. Earlier, data teams of the health organization had to dive into each letter manually and maintain records. Maruti Techlabs came to the rescue. 

Its ML model used NLP to identify and divide letters based on the context scanned through texts, images, etc. The NLP model could interpret words and match them with assigned entries, making data sorting and management a much easier job for the client.

7. Social Media Monitoring for Crisis Management

NLP has become a surveillance tool as well. Governments are using it to keep a sharp eye on social media platforms. In case any crisis is detected, they can respond faster than ever before. Now, that’s not the only case. NLP uses are coming in handy to counter misinformation and rumors as well.

Case Study: 

DisTrack is a semi-automated tool using the capabilities of NLP to identify and track misinformation on social networks. The goal of this tool is to track the misinformation to its origin and identify the influence of various factors contributing to the spread of misinformation within the assigned network. 

DisTrack’s architecture includes methodologies like keyword research, graph generation techniques, and more blended with NLP. DisTrack has a proven history of demonstrating its capabilities in three scenarios: Russia/Ukraine Conflict, Anti-Vaccine Rumors, and Hate Speech.

8. Legal Document Review

NLP use cases have become an accessory for law firms to review, summarize, and draft legal documents faster. The integration of NLP enables the speedy development of legal drafts without having to put in too much manual effort. Using NLP, AI systems can build better contextual understanding around topics and create factually correct drafts.

Case Study:

Luminance, an AI-powered legal tool, is used by a Norwegian law firm called BA-HR to automate the process of identifying clauses and extraction. It also ensures the accuracy of legal drafts written in the Norwegian language eliminating the requirement of lengthy processes or third-party interferences within the process.

8. Search Engine Optimization (SEO)

NLP is making search engines smarter by giving them the ability to understand the context, semantics, and sentiments of user queries. This helps them find more curated results and move beyond just keyword-centric responses. 

With the integration of NLP, search engines are also capable of understanding the context of drafts well and judging their ability to deliver information better. This also eliminates traditional practices like keyword stuffing that were earlier ruining the quality of content.

Case Study:

In 2019, Google launched Bidirectional Encoder Representations from Transformers (BERT) models and integrated them into the search engine. Transformer model development results in smarter AIs using NLP. This leads to a better understanding of the context of user queries and the delivery of results using search engines.

NLP helped Google judge better whether a person was looking for information about some concept or some entity. As a result, you get to witness more contextual and accurate results. The above image demonstrates an NLP search example. Even the shortest query, ‘Google CEO,’ is enough for the search engine to build the context of the user’s intent and provide results.

10. Education and eLearning

AI in education is rising at a CAGR of 33.5% between 2024 and 2029. The reason? Its ability to continuously revolutionize the quality of education delivery. Beyond that, using technologies like NLP, AI-powered education is also becoming more accessible. NLP helps deliver personalized content, understand multiple languages, and meet individual learner needs. As a result, modern learning management systems are getting smarter.

Case Study:

Alphary, an Austrian edtech startup, implemented NLP use cases to redefine the education landscape. Alphary collaborated with Oxford University to come up with a redefined user journey that would take advantage of NLP by building an app.

It hired Intellias to develop an app that could deliver personalized automated feedback for language learning enthusiasts. The app analyzed user responses, expressions, learning preferences, etc, and simplified the user journey.

11. Employee Recruitment and Resume Screening

NLP is accelerating the speed of finding ideal candidates. The tech automates the screening of candidates’ resumes, helping recruiters close hiring processes faster. The process enhances efficiency while reducing the investment of resources in parallel. 

NLP algorithms can make conclusions from cover letters, summarize the backgrounds of candidates, identify the relevancy of their professional history, and more.

Case Study:

Unilever used AI to screen resumes, conduct digital interviews, and assess candidates’ psychometric profiles. Unilever also integrated a series of games in the recruitment process powered by Pymetrics. 

The game was centered around observing candidates’ cognitive and emotional traits. HireVue also helped Unilever organize AI-powered interviews in the next stage. The data gathered from these interviews was used to observe interview content, candidate’s facial expressions, body language, etc.

12. Voice Recognition for Accessibility

Among one of the top natural language programming examples stands its contribution to boosting inclusivity. NLP plays a critical role in improving the quality of voice-based inputs. As a result, NLP can understand accents, dialects, and languages better. To put it in perspective, NLP enhances the quality of voice-controlled systems and delivers more accurate results without annoying users.

You might have seen this video going viral where popular Punjabi actor and singer Diljit Dosanjh tries to interact with Alexa. His unique Punjabi accent, mixed with a sprinkling of English words, becomes challenging for the system to interpret.

In a humorous twist, the assistant struggles to grasp his commands, leaving him hilariously frustrated. This relatable moment highlights a real issue many users face when voice systems fail to understand regional accents or dialects.

Case Study:

Google’s Project Relate takes advantage of NLP to make digital assets more inclusive. In addition to its core functionalities, like assistive tools for inclusive communication and higher-quality transcriptions, it also aligns with Web Content Accessibility Guidelines (WCAG). 

This ensures that individuals with speech impairments can navigate and interact with digital platforms effortlessly. By combining cutting-edge NLP with accessibility standards, Project Relate sets a benchmark for inclusive technology.

13. Product Categorization in E-commerce

The AiCommerce market is rising at a CAGR of 24.34% between 2024 to 2034. With AI joining the commercial sector, NLP makes product categorization a much easier job. Now, using data such as product descriptions, ratings, reviews, and more, store owners can easily categorize them under various filters. NLP can use these details to assign products to their respective categories.

Case Study:

ACL Anthology has a case study talking about a team of developers that developed the LLM-based Dual-Expert Classification Paradigm for the purpose of e-commerce product categorization. The framework uses domain-specific knowledge blended with fine-tuning techniques to categorize products for easier findings.

14. Customized Marketing Campaigns

Data-driven marketing trends are dominating the globally spread web of businesses. Modern marketers are increasingly collaborating with natural language processing companies. Their aim is to build and deploy NLP solutions that can help them gather user intent and sentiment about any specific products or services. The sources to collect data are social media platforms, review platforms, and more. Pairing data with predictive analytics software helps them plan more actionable strategies.

This not only helps in identifying trends and customer pain points but also enables brands to predict purchasing behavior. As a result, businesses can craft more personalized marketing campaigns that resonate deeply with their target audience, boosting engagement and conversions.

Case Study:

The parent company of giants in the fast food industry, like Taco Bell, KFC, and Pizza Hut- Yum Brands, adopted AI-driven marketing strategies to increase their sales and customer retention rates. 

In an interview with The Wall Street Journal, Joe Park, The chief digital and technology officer at Yum, stated how AI-powered systems are scanning the context using NLP and assigning relevant questions to customers. Since the implementation of powerful AI systems, the company, according to him, has multiplied its impact in the market.

15. Movie and Script Analysis in Hollywood

NLP is automating the scripting process. It offers the ability to automate complex manual processes, such as designing character personalities, making dialogs and scenes more impactful, etc. Using models like Bi-LSTM, GPT-3, and AI-CMO, storytelling is streamlined while creative integrity in parallel is maintained as well. 

Case Study:

Ghosh et al. (2021) extracted movie titles from diverse poster datasets to develop a CNN-based model. The research highlighted NLP’s ability to process graphic-rich text accurately, showing its capacity to automate creative tasks. 

We also tried to generate a script using the prompt:

‘Write a short film script about a boy lost in space in a spacesuit but with no access to any spaceship. His goal? He uses the motion of his float to survive and make his way to somewhere where he can survive. He tries reaching the space station first, but it's too far; then, he decides to dive into the orbit of a nearby Earth-like planet in hopes that it has some rover, increasing his chance of survival. But the movie ends with a twist.’

The result? 

Read the entire script here: Drift.

Wrapping Up

The transformative abilities of NLP use cases continue to shape industries worldwide. From Hollywood to the food industry, the impact of this tech is seen everywhere. In parallel, apart from improving the quality of services, it is changing the way people interact with devices- which is now much more efficient than it used to be. 

As Alexey Nikolayev, SVP of NIX, says:

‘Machine learning, natural language processing, computer vision, and other cool rocket science technologies are much more accessible for application developers compared to 5 years ago. With AWS Sagemaker or IBM Watson, we can build applications with previously unthinkable capabilities. This opens terrific opportunities for AI augmentation of many “conventional” jobs and businesses.’ 

And that statement is indeed endorsing the fact that we have grown from a world of landlines to a world of AI-powered chatbots at an unimaginable speed. Now that systems like Bi-LSTM, GPT-3, and AI-CMO are in place, and we live in an era where governments are announcing $500 billion investments just for AI-driven solutions, the quality of NLP is going to improve further. 

So, to put all of this into perspective, it is going to be exciting to see how efficient these NLP use cases will be in the future.