Complete Guide to Natural Language Processing NLP with Practical Examples

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There’s a lot to be gained from facilitating customer purchases, and the practice can go beyond your search bar, too. For example, recommendations and pathways can be beneficial in your e-commerce strategy. Machine Translation is the procedure of automatically converting the text in one language to another language while keeping the meaning intact.

  • US retailer Nordstrom analyzed the amount of customer feedback collected through comments, surveys and thank you’s.
  • Well, because communication is important and NLP software can improve how businesses operate and, as a result, customer experiences.
  • These functionalities have the ability to learn and change based on your behavior.
  • Plus, tools like MonkeyLearn’s interactive Studio dashboard (see below) then allow you to see your analysis in one place – click the link above to play with our live public demo.
  • Closely linked with speech recognition, chatbots are another useful business tool powered by NLP.
  • Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

It is important to test the model to see how it integrates with other platforms and applications that could be affected. Additional testing criteria could include creating reports, configuring pipelines, monitoring indices, and creating audit access. The first step is to define the problems the agency faces and which technologies, including NLP, might best address them. For example, a police department might want to improve its ability to make predictions about crimes in specific neighborhoods.

Part of Speech Tagging

Discover how to make the best of both techniques in our guide to Text Cleaning for NLP. As you can see in our classic set of examples above, it tags each statement with ‘sentiment’ then aggregates the sum of all the statements in a given dataset. I hope you can now efficiently perform these tasks on any real dataset. The tokens or ids of probable successive words will be stored in predictions.

NLP allows a store to capture context and add contextually relevant synonyms to search results. It helps the store predict what its customers are looking for and Natural Language Processing Examples in Action highlight relevant listings. Branched out of artificial intelligence (AI), natural language processing (NLP) works on communication between humans and machines.

NLP Chatbot and Voice Technology Examples

Chatbots are everywhere these days – on the websites you browse, in messenger platforms, and in apps – and the technology is helping to streamline a range of business processes, including customer service, sales, and even HR. If you’ve interacted with a brand via messaging lately, chances are you were chatting with a bot. And although the technology is far from perfect, it’s definitely getting harder to tell whether we’re talking to a human or a computer. Many of the tools that make our lives easier today are possible thanks to natural language processing (NLP) – a subfield of artificial intelligence that helps machines understand natural human language. Financial services company American Express utilizes NLP to spot fraud. The system examines multiple text data types to find patterns suggestive of fraud, such as transaction records and consumer complaints.

If you’re interested in learning more about how NLP and other AI disciplines support businesses, take a look at our dedicated use cases resource page. NLP customer service implementations are being valued more and more by organizations. The tools will notify you of any patterns and trends, for example, a glowing review, which would be a positive sentiment that can be used as a customer testimonial. To better understand the applications of this technology for businesses, let’s look at an NLP example. Wondering what are the best NLP usage examples that apply to your life? Spellcheck is one of many, and it is so common today that it’s often taken for granted.

Text Classification

From setting our morning alarm to finding a restaurant for us, a voice assistant can do anything. They have opened a new door of opportunities for both users and companies. Additionally, it can reduce the cost of hiring call center representatives for the company.

He is a data science aficionado, who loves diving into data and generating insights from it. He is always ready for making machines to learn through code and writing technical blogs. His areas of interest include Machine Learning and Natural Language Processing still open for something new and exciting.

Why is Natural Language Processing Important?

This significantly speeds up the hiring process and ensures the best fit between candidates and job requirements. But the technology is getting better and better, and there are a variety of tools to help you accomplish exactly the kind of summarization you need. There are even chrome extensions that can help you out, though it might be hard to scale content summaries that way. It crawls individual pieces of content using NLP to flag thin content and suggests opportunities to deepen your topic coverage. It will even suggest subtopics to cover, as well as questions to answer and primary and secondary keywords to include.

Text summarizations can be used to generate social media posts for blogs as well as text for newsletters. Marketers can also use it to tag content with important keywords and fill in other metadata that make content more visible to search engines. HootSuite is a social media management platform that includes sentiment analysis as part of its tracking functionality. Once you’ve posted content, Hootsuite will track it for the usual analytics as well as positive or negative reactions to your content. MonkeyLearn is a good example of a tool that uses NLP and machine learning to analyze survey results. It can sort through large amounts of unstructured data to give you insights within seconds.


Our accessible and effective natural language processing solutions can be tailored to any industry and any goal. The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs. Just like you, your customer doesn’t want to see a page of null or irrelevant search results.

Social media monitoring represents a great opportunity for companies to know what their clients are talking about on social media platforms, blogs, etc. and to discover relevant information for their business. By interacting with clients, processing their conversations and essentially understanding customers in their own words, companies can better understand their customers’ needs and improve the relationships with them. AI is a general term for any machine that is programmed to mimic the way humans think.

Real-Life Examples of NLP in Action

Despite the impressive advancements in NLP technology, there are still many challenges to overcome. One of the biggest obstacles is the inherent ambiguity of human language. Words and phrases can have multiple meanings depending on context, tone, and cultural references. NLP algorithms must be trained to recognize and interpret these nuances if they are to accurately understand human language. More and more people these days have started using social media for posting their thoughts about a particular product, policy, or matter. These could contain some useful information about an individual’s likes and dislikes.

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