Brief About Top Applications of NLP Across Industries

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Applications of NLP (Natural Language) is the communication method we, humans often use. We have developed machines that can interpret and communicate as we do. 

In this article, we will have a deeper understanding of “What is NLP and its applications in real life?”. 

NLP is a branch of Artificial Intelligence and stands for Natural Language Processing. It refers to the method of machines understanding, translating, and manipulating natural language.

NLP combines computational linguistics and computer science. It develops computers capable of understanding written and spoken dialects like humans.

Top Applications of NLP across industries

What is Natural Language Processing used for?

NLP along with machine learning and deep learning are important aspects of Artificial Intelligence. NLP was established in the early 1900s and has been through different stages of development.

Around the 1950s, the evolution of NLP and computers inspired the idea of AI. Alan Turing wrote a paper discussing the possibility of a “thinking” machine. 

NLP has gained credibility in the industry and has brought upon ideas as well as discoveries. With the rise of information, we have developed machines that can translate data sets into actionable information, and NLP plays a pivotal role here.

Importance of NLP:

  • There are large volumes of unstructured data from various sources. NLP automates the process of efficiently analyzing text and speech. 
  • Machines are capable of working 24/7 also can be trained towards various goals and agendas. With the help of NLP, machines can interpret and manipulate hundreds of languages and dialects.
  • NLP has been advanced and developed over the years. Which allows non-technical individuals to use computing systems and acquire data.

How does NLP work?

There are more than 6500 registered human languages. Each has different grammar, syntactic, and semantic rules.

NLP uses large amounts of data to prepare the computer for conversations. Hence it employs multiple techniques that help ease the process.

Here are the techniques that help NLP to train a computer in natural language:

  • Data Preprocessing:

Before machines interpret natural language, they first need to understand it. The process of interpretation and understanding is known as data-preprocessing.

The method segments different languages for a better understanding. Hence if a machine is instructed to translate from English to Mandarin or vice versa it uses the data.

  • Segmentation and tokenization of Sentences:

The method allows computers to learn the different permutations and combinations of sentences. Sentences with similar words might have very different meanings.

For example;

I left my phone on the table.
My phone is on the left side of the table.

Here, the word “left” has two different meanings. Hence, computers need to learn the differences and create algorithms for sentences and their context.

  • Part of Speech Tagging (POS Tagging):

The method analyzes and classifies words and sentences according to the parts of speech. It marks the words that play a role of a noun, verb, adjective, etc in a sentence.

  • Lemmatization:

The process is simple and ties down the words to their original form. The process uses etymology to its advantage.

English is one of the common languages in the world. It borrows a lot of its words from different languages.

For example;
Ectomy is a Greek word that refers to the process of cutting out. Adding a prefix to the word would give it a different meaning.
Gastrectomy – it is the process of surgically removing the stomach.
Iridectomy – also known as corectomy, is the surgical removal of part of the iris.

  • Removing Stop words:

This method removes the words that do not add any meaning or additional information in a sentence. Prepositions and articles like at, to, a, the, etc.

TF-IDF is term frequency-inverse document frequency. It is the algorithm that discovers stop words in a sentence.

Here are the Real-Life Applications of NLP Across Industries

NLP in Marketing and Advertising

Marketing and Advertising are two very crucial methods of promoting businesses. It creates opportunities for businesses to reach the right audiences.

The most practical application of NLP in marketing is through social media. Businesses are heavily utilizing NLP techniques to analyze posts and understand their customers’ profiles and requirements. 

NLP has also made it easier for companies to understand customer pain points. The technique gives a unique perspective to businesses and provides appropriate solutions to customers.

  • Sentiment Analysis:

A major example of the NLP technique is Sentiment Analysis. It is a process of measuring the audience’s emotions through social platforms and text analysis.

  • Chatbots:

Chatbots have been a pivotal part of customer service. It has helped companies to work on their products and services through recorded feedback.

Chatbots are a major part of applications and websites. It is an easy and quick solution to solve customer queries.

  • Online Advertisements:

Online advertising giants like Google and Facebook use NLP to target customers using Keyword Matching. Here, they dedicate online advertisements to audiences that associate with certain keywords or phrases that will make them a potential customer.

Apart from this, remarketing ads to audiences and retargeting interested leads are also some of the techniques used through NLP.

NLP in Finance

NLP has brought on a revolution in the Finance Industry. From financial institutions to investment companies to the stock market all are now leveraging NLP to meet their objectives.

According to Mikey Shulman, MIT Sloan, finance lecturer, “It’s such a fast-moving field, a lot of what’s state-of-the-art now wasn’t invented when I taught the course a year ago. Especially in finance, data that can help make timely decisions come in the text.  A company will release its report in the morning, and it will say, ‘Our earnings per share were a $1.12.’ That’s a text by the time that data makes its way into a database of a data provider where you can get it in a structured way, you’ve lost your edge. Hours have passed.”

He believes that NLP could deliver faster solutions and data relevant for real-time issues and queries. Companies would have a competitive advantage and would push their analysts to focus on important tasks and not just wait for data.

Here are a few applications of NLP in the financial business:

  • Banking Sector:

Deutsche Bank used NLP to improve its environmental investing process. NLP helped the bank enhance the discovery process by training the machines with their company and industry-specific vocabulary. 

  • Stock Market:

The Stock Market heavily depends on data analysis. Companies and individuals both invest in the market based on their analysis of the previous results.

Here instead of just interpreting natural language, NLP also uses techniques like tokenization to translate financial and stock market data.

It provides data for turnover analysis, factor returns, conversion to a data frame, etc to help determine the faith of the stocks.

NLP in Healthcare

The healthcare industry heavily depends on new technologies to enhance its services. NLP may not be a new concept, but it was recently introduced in the healthcare industry.

NLP has paved the way for multiple opportunities in healthcare. It helps with understanding human speech and interpreting the meaning.

It has also helped structure data in the databases. NLP has mapped out the documents to enable professionals to make decisions and analyze information.

NLP has helped professionals with speech recognition technologies. This reduces the hassle of making notes and increasing the chances of errors while dictation.

NLP and Machine Learning have transformed the documentation process in healthcare. The concepts have structured EHRs (Electronic Health Records) with the help of speech-to-text dictation.

NLP also helps ease the burden on health IT systems by regulating reports through automated registry reporting. ARR identifies the ejected fraction in health documents and saves values that are important for analysis.

NLP-driven Computer-assisted Coding enhances coder accuracy. CAC segregates patients’ information to maximize claims and provide solutions for the value-based paradigm. 

NLP in Human Resources

The human resources department is an essential part of the organization. It takes care of the recruitment, retention, engagement, feedback, appraisals, etc of an employee.

The bigger the organization, the more employee data available. It is critical to keep a track of the documents and information of the employee.

Here are a few important roles of NLP in HR:

  • NLP helps maintain data that classifies an employee according to their qualifications and hierarchy. It helps identify employees and treat them as individuals in the system.
  • NLP plays a very important role when it comes to the career development of the employee. Whether the employee fits the role they applied for or requires the right training to move forward.
  • The department also helps improve employee engagement and retain potential employees through simple surveys and feedback. The surveys help understand the employees’ perceptions and create a bond of trust.

NLP in Education

In recent years, there has been significant growth for E-learning platforms. Students and professionals are opting to learn from online platforms to improve their skill sets or gain new ones.

NLP not only identifies potential in students through their data but also evaluates their performances. The evaluation process bridges the gap between standard and actual results by recognizing what the student requires to reach their highest potential. 

The process helps students recognize their potential and motivates them to work towards a realistic goal. Teachers can also discover newer learning techniques for students and help them adapt to different learning methods.

According to Scott Crossley, Applied Linguistics and Learning Sciences at Georgia University, “Beyond improving students’ language skills directly, NLP features can also be used to help educators better understand what is happening cognitively with their students. NLP can thus both improve the quality of instruction within individual assignments and help educators improve the learning environment more broadly.”

Other NLP Applications in Real-Life

Applications of NLP are a vast concept and could be found in the IT Industry and helping various businesses. Apart from being pivotal to other industries, NLP has played an important role in the IT Industry.

It has solved problems like text classification and improved predictive texts. It has helped Search engines like Google to correct search keywords and help users find what they are looking for.

Perhaps, NLP has shown tremendous progress with Deep Learning and has improved the services of Voice Assistants like Alexa, Siri, and Google assistant. It helps with Machine Translation, which is interpreting one language text or speech to another.

In Foundations of Statistical Natural Language Processing, 1999, “Machine translation, the automatic translation of text or speech from one language to another, is one [of] the most important applications of NLP.” 


According to the Markets and Markets analysis, NLP is expected to grow from $11.6 billion in 2020 to $35.1 billion by 2026. Applications of NLP show potential as it combines technology and human languages to improve multiple industries and their products, solutions, and services.

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