Natural language processing applications

Businesses require data, but most of that data is unorganized and inaccessible. Natural Language Processing (NLP) has proven to be a lifeline in this scenario. Many businesses are using Natural Language Processing (NLP) because of the numerous commercial and growth prospects it provides. This article will look at this incredible AI-powered technology and how it can benefit your company. You can also use the Table of Contents to jump to any topic you’re interested in using the links below.

Defining NLP

NLP in artificial intelligence (AI) technique enables a machine to understand and recognize the intricacies of human language. It attempts to understand various lexicons, grammatical syntaxes, and the relationships between words and sentences in the same way a person does. Before organizing it, it analyses unstructured material for relevance, spelling discrepancies, correlation, and semantic significance. And he recalls it.

There are so many Natural language processing applications in business that we don’t even realize how widespread the technology is. Speech pattern recognition, weather forecasting, healthcare applications, and identifying handwritten texts exemplify how NLP is employed today. Siri and Alexa, our car navigation system that gives us the quickest route, our favorite OTT streaming channel that recommends movies to watch, autocomplete predictive texts on our phones, and translation applications are all examples of how NLP has become ingrained in our life.

What Is The Purpose Of NLP?

NLP analyses numerous components of human languages, such as syntax, semantics, pragmatics, and morphology, to comprehend its structure and meaning. Then, using computer science, this linguistic information is transformed into rule-based machine learning algorithms that can solve specific issues and complete jobs.

Consider the case of Gmail. Thanks to an NLP process known as keyword extraction, emails are automatically classified as Promotions, Social, Primary, or Spam. By “reading” phrases in subject lines and connecting them with specified tags, machines automatically learn which category to assign emails to.

Advantages Of NLP

There are numerous advantages to NLP, but here is a handful that will help your company become more competitive:

Conduct a large-scale investigation: Natural language processing (NLP) allows machines to understand and assess enormous amounts of unstructured text data, such as social media comments, customer service issues, online reviews, news articles, and so on.

Real-time automation is possible: Natural language processing techniques can assist machines in learning to organize and route information with little to no human contact — quickly, effectively, precisely, and at all hours of the day and night.

Make NLP tools specific to your sector: Complex, industry-specific terminology – including sarcasm and misused terms – can be adapted to your demands and criteria using natural language processing algorithms.

What Are Some Examples Of NLP In The Workplace?

Let’s look at some modern business Natural language processing applications to examine how the technology has impacted these businesses and their processes.

Sentiment Analysis On Social Media

NLP for social media listening is unique because it recognizes online short forms (LOL, BRB, TL;DR), slang, code-switching, emoticons and emojis, and hashtags. NLP can extract information from it and prepare it for an ML model to consume whatever language your consumers use. Sentiment analysis can also determine how well your brand performs based on the positive, negative, or neutral feelings that it detects in your social media mentions.

As a result, it provides you with helpful information. Based on public opinion resulting from social media monitoring, you can reach out to an influencer as part of your marketing strategy, change your advertising campaign, improve your product or service features, and upscale your brand reputation.

Healthcare And Patient Voice

Hospitals and healthcare professionals use NLP technology more frequently than ever to gather and manage patient notes and electronic health data (EHRs). Using textual data from in-clinic questionnaires, post-appointment surveys, and feedback web forms, AI/ML models examine patient input, waiting room experience, post-surgery treatment, opinions, and feelings. Patient feedback allows them to assess the quality of their service and improve the patient experience. Learn about the broader applications of NLP in healthcare.

Language Translation

Information and knowledge can be found in various places, but not everyone can speak numerous languages fluently. That is why, for the most part, online translations are a boon, particularly for researchers. Without NLP technology offering speech-to-text translations at scale so rapidly and efficiently, we wouldn’t be able to watch the various foreign films and documentaries with subtitles on our video streaming channels. Linguists are deeply invested in the morphology, anthropological linguistics, philology, syntax, and phonology of languages because they are beautiful, unique, and intricate. They are always learning new things, which helps data scientists build AI/ML models for language translations.

Sentence Boundary Detection (SBD) is an NLP task that recognizes the borders of a set of words. It is one of the most fundamental translation tasks. That is why you can translate complete documents into many languages and have them match phrase for sentence. Five hundred million users use Google Translation to translate documents or text into 100 different languages. There is a slew of additional translation apps and websites to choose from.

On the other hand, machine translation has a long way to go due to the complexity of human language, particularly in languages where no spaces are used to indicate the end of a word, such as Japanese or Thai. Data scientists are continually working on perfecting word border detection.

Text Analytics

Text analytics is used by businesses to get insights from any sources of information that are relevant to them. This deluge of information can come from various sources, including news, social media reviews, tweets, online surveys, voice-to-text notes, and more. NLP transforms this unstructured input into usable documentation that a machine learning program can analyze. Semantic Search also aids in comprehending the meaning and intent of words and sentences. When used in conjunction with NER, text analytics matches a sentiment to an entity, allowing you to learn how a third party feels about you. This data can give you actionable insights to help you make better business decisions.

OCR Stands For Optical Character Recognition

Emails, invoices, service agreements, research papers, human resource documents, purchase orders, and other textual formats can all be used to acquire raw data for text analytics. However, it may also be found in video formats on YouTube, Igtv, Facebook, and TikTok and in photographs (think Instagram or Pinterest). Natural Language Processing algorithms use Optical Character Recognition (OCR) technology for photos and Video Content Analysis to analyze this image-based data. The technique turns the scanned file into a text-searchable file, which aids the machine model in deriving insights from the data.

Top Natural Language Processing Applications

Market Intelligence 

Marketers can utilize natural language processing to understand their clients better and use those insights to develop more effective tactics. They can analyze subjects and keywords and make effective use of unstructured data thanks to the power of NLP. It can also determine your consumers’ pain points and maintain track of your competition.

Sentiment Analysis

Companies can regularly use sentiment analysis to acquire a better knowledge of their business. Humans can be sarcastic and sardonic during conversations. You may keep an eye on social media mentions and use real-time sentiment analysis to intervene before things get out of hand. Your company may sense the pulse of its customers with this NLP application. It also allows you to evaluate how your clients reacted to your most recent digital marketing campaign.

Recruiting And Hiring

We can all agree that picking the right staff is one of the most important duties performed by the HR department. However, HR has so much data in the current situation that sifting resumes and shortlisting prospects become overwhelming.

Natural Language Processing can help to make this work more accessible. HR experts can use information extraction and named entity recognition to extract information from candidates, such as their names, talents, locations, and educational histories. This enables unbiased resume filtering and the selection of the best candidate for the job.

Text Summarization

This NLP application extracts the most crucial information from a text and summarises it. The primary purpose is to speed up sifting through massive volumes of data in news articles, legal documents, and scientific studies. Text summarization can be done in two ways: extraction-based summarization, which selects crucial words and provides a summary without adding further information, and abstraction-based summarization, which paraphrases the original content to produce new terms.

Survey Analysis 

Surveys are an essential tool for businesses to use in evaluating their performance. Survey analysis is crucial in finding defects and supporting companies in improving their goods, whether gathering input on a new product launch or analyzing how effectively a company’s customer service is doing.

When many clients complete these surveys, the issue emerges, resulting in massive data. The human brain is unable to comprehend everything. At this time, natural language processing is introduced. These methods help organisations get accurate information about their consumers’ opinions and improve their performance.


NLP has revolutionized the way businesses operate. It can boost your business and guide you on the proper route. It improves operational efficiency, scalability, agility, and resource allocation. As more enterprises use AI-powered machine learning models, it’s time to assess the efficacy of your legacy models. You can be at the forefront of technological breakthroughs and become a market leader by implementing cognitive technologies such as NLP.