Ends soon! Grow on your schedule with big savings on Coursera Plus. Save 40% for 3 months . Natural language processing ensures that AI can understand the natural human languages people speak every day. Learn more about this impactful AI subfield. Natural language processing (NLP) is a subset of artificial intelligence, computer science, and linguistics that allows computers to understand human language, whether you write it, speak it, or even scribble it. Natural language processing (NLP) is a subset of artificial intelligence, computer science, and linguistics that allows computers to understand human language, whether you write it, speak it, or even scribble it. Several benefits of natural language processing in AI include its ability to analyze structured and unstructured data, identify customer insights using sentiment analysis, and help an organization understand its target market by analyzing social media posts and reviews. Several benefits of natural language processing in AI include its ability to analyze structured and unstructured data, identify customer insights using sentiment analysis, and help an organization understand its target market by analyzing social media posts and reviews. Natural language processing works by using techniques such as sentiment analysis, summarization, keyword extraction, and tokenization. Natural language processing works by using techniques such as sentiment analysis, summarization, keyword extraction, and tokenization. Natural language processing software includes Google Cloud NLP API, IBM watsonx, and Amazon Comprehend. If you’ve decided to begin developing your NLP skill set, enroll in the Natural Language Processing Specialization offered by DeepLearning.AI. In as little as three months, you can learn about large language modeling, applied machine learning, artificial neural networks, TensorFlow, and more. What is natural language processing? NLP is a subset of artificial intelligence, computer science , and linguistics focused on making human communication, such as speech and text, comprehensible to computers. NLP is used in a wide variety of everyday products and services. Some of the most common technologies that use NLP are voice-activated digital assistants on smartphones, email-scanning programs used to identify spam, and translation apps that decipher foreign languages. Read more: Using ChatGPT as a Personal Assistant: Work Smarter, Not Harder What are NLP techniques? NLP encompasses a wide range of techniques to analyze human language. Some of the common techniques you will likely encounter in the field include: Sentiment analysis: An NLP technique that analyzes text to identify its sentiments, such as “positive,” “negative,” or “neutral.” Sentiment analysis is commonly used by businesses to better understand customer feedback. Sentiment analysis: An NLP technique that analyzes text to identify its sentiments, such as “positive,” “negative,” or “neutral.” Sentiment analysis is commonly used by businesses to better understand customer feedback. Summarization: An NLP technique that summarizes a longer text in order to make it more manageable for time-sensitive readers. Some common texts this technology can summarize include reports and articles. Summarization: An NLP technique that summarizes a longer text in order to make it more manageable for time-sensitive readers. Some common texts this technology can summarize include reports and articles. Keyword extraction: An NLP technique that analyzes a text to identify the most important keywords or phrases. Keyword extraction is commonly used for search engine optimization (SEO) , social media monitoring, and business intelligence purposes. Keyword extraction: An NLP technique that analyzes a text to identify the most important keywords or phrases. Keyword extraction is commonly used for search engine optimization (SEO) , social media monitoring, and business intelligence purposes. Tokenization: The process of breaking characters, words, or subwords down into “tokens” that the program can analyze. Tokenization undergirds common NLP tasks like word modeling, vocabulary building, and frequent word occurrence. Tokenization: The process of breaking characters, words, or subwords down into “tokens” that the program can analyze. Tokenization undergirds common NLP tasks like word modeling, vocabulary building, and frequent word occurrence. Whether it’s being used to quickly translate a text from one language to another or producing business insights by running sentiment analysis on hundreds of reviews, NLP provides both businesses and consumers with a variety of benefits. Unsurprisingly, then, you can expect to see more of it in the coming years. According to research by Fortune Business Insights, they project the global market for NLP to grow from $45.74 billion in 2026 to $193.4 billion in 2034 [ 1 ]. Some common benefits of NLP include: The ability to analyze both structured and unstructured data , such as speech, text messages, and social media posts. The ability to analyze both structured and unstructured data , such as speech, text messages, and social media posts. Improving customer satisfaction and experience by identifying insights using sentiment analysis. Improving customer satisfaction and experience by identifying insights using sentiment analysis. Reducing costs by employing NLP-enabled AI to perform specific tasks, such as chatting with customers via chatbots or analyzing large amounts of text data. Reducing costs by employing NLP-enabled AI to perform specific tasks, such as chatting with customers via chatbots or analyzing large amounts of text data. Better understanding a target market or brand by conducting NLP analysis on relevant data like social media posts, focus group surveys, and reviews. Better understanding a target market or brand by conducting NLP analysis on relevant data like social media posts, focus group surveys, and reviews. NLP limitations NLP can be used for a wide variety of applications, but it's far from perfect. In fact, many NLP tools struggle to interpret sarcasm, emotion, slang, context, errors, and other types of ambiguous statements. This means that NLP is mostly limited to unambiguous situations that don't require a significant amount of interpretation. Natural language processing examples Although natural language processing might sound like something out of a science fiction novel, the truth is that NLP examples already exist in your everyday life as you interact with countless NLP-powered devices and services every day. Online chatbots, for example, use NLP to engage with consumers and direct them toward appropriate resources or products. While chatbots can’t answer every question that customers may have, businesses like them because they offer cost-effective ways to troubleshoot common problems or questions that consumers have about their products. Another common use of NLP is for text prediction and autocorrect, which you’ve likely encountered many times before while messaging a friend or drafting a document. This technology allows texters and writers alike to speed up their writing process and correct common typos. Is ChatGPT an NLP? ChatGPT , a chatbot powered by AI and natural language processing, produces unusually human-like responses. Recently, it has dominated headlines due to its ability to produce responses that far outperform what was previously commercially possible. If you'd like to learn more, the University of Michigan's ChatGPT Teach Out brings together experts on communication technology, the economy, artificial intelligence, natural language processing, health care delivery, and law to discuss the impacts of the technology now and into the future. Natural language processing applications NLP has a wide array of applications across various sectors such as finance, insurance, and health care. Prominent applications for NLP technology include voice-activated assistants, machine translation, sentiment analysis, chatbots, virtual customer support, classification and categorization, content recommendation systems, text summarization, speech recognition, and natural language generation. Here are just some of the ways natural language processing is used in the real world:

1. Voice-activated assistants NLP powers virtual assistants, so if you ever use Apple’s Siri, Amazon’s Alexa, and IBM’s watsonx assistant, you’ve already experienced NLP. This technology enables them to understand and respond to voice commands. It allows you to interact with your device using natural language to perform tasks, search for information, and control smart home devices. 2. Machine translation NLP is the driving force behind machine translation services such as Google Translate. It allows for the automatic translation of text and speech between languages, making global communication more accessible. NLP allows an online translator to understand the individual rules of grammar and language structure between two languages and effectively decode one into the other. 3. Sentiment analysis Your business can use NLP for sentiment analysis to gauge a customer’s opinion, their satisfaction, and the market’s response to your products by analyzing social media posts, customer reviews, and survey responses. This can help your company make better decisions, especially when formulating future strategies. 4. Chatbots and virtual customer support NLP enables chatbots to understand and respond to customers' questions and comments in a conversational manner. This application is widely used in customer service to provide instant assistance, book appointments, and resolve common issues. 5. Content recommendation systems Platforms like T-Mobile, Spotify, and Disney+ use NLP-based recommendation systems to analyze user preferences and provide personalized content suggestions based on previous interactions and the content's textual data. Using sentiment analysis, also powered by natural language processing, recommendation systems can even recommend movies, music, or other media based on how users have reviewed those products. 6. Speech recognition Speech recognition assists with converting spoken language into text in real time, which is essential for dictation software, hands-free computing, and real-time transcription services. Everybody talks a little differently: At different speeds, in varying tones, with accents and regional dialects, and with differing pronunciations. After the speech recognition software transcribes your words, natural language processing analyzes those words, determines the meaning behind them, and then formulates an appropriate response. 7. Natural language generation (NLG) This involves using NLP to generate natural language text from data, enabling applications like automated report generation, personalized content creation, and article writing. NLG can also craft stories. With natural language generation, you can ask an AI language model like Amazon’s Alexa or Apple’s Siri a question as if speaking to another person. The model will respond similarly. These applications demonstrate the versatility and impact of NLP in simplifying interactions, enhancing accessibility, and providing deeper insights from textual data across diverse domains. Natural language processing software You can choose from the numerous natural language processing tools and services available to help you start working with NLP today. Some of the most common tools and services you might encounter include the following: Google Cloud NLP API Google Cloud NLP API Amazon Comprehend Amazon Comprehend Natural language processing with Python Python is a programming language well-suited to NLP. Some common Python libraries and toolkits you can use to start exploring NLP include NLTK, Stanford CoreNLP, and Gensim. Discover more with our free resources Subscribe to our weekly LinkedIn newsletter, Career Chat , for industry updates, tips, and trends. Then, explore free resources related to NLP to optimize your professional growth: Watch on YouTube: Stemming vs. Lemmatization: NLP Made Simple Watch on YouTube: Stemming vs. Lemmatization: NLP Made Simple Read our Career Chat issue: Open for a Primer on AI, ChatGPT, and More Read our Career Chat issue: Open for a Primer on AI, ChatGPT, and More Begin your journey: Natural Language Processing Learning Roadmap: Beginner to Expert Begin your journey: Natural Language Processing Learning Roadmap: Beginner to Expert With Coursera Plus , you can learn and earn credentials at your own pace from over 350 leading companies and universities. With a monthly or annual subscription, you’ll gain access to over 10,000 programs—just check the course page to confirm your selection is included. Break down barriers to learning with big savings Get Coursera Plus Break down barriers to learning with big savings Article sources Fortune Business Insights. “ The global natural language processing (NLP) market , https://www.fortunebusinessinsights.com/industry-reports/natural-language-processing-nlp-market-101933.” Accessed August 4, 2026. Fortune Business Insights. “ The global natural language processing (NLP) market , https://www.fortunebusinessinsights.com/industry-reports/natural-language-processing-nlp-market-101933.” Accessed August 4, 2026. Exploring Natural Language Processing Applications December 6, 2024 9 Essential Natural Language Processing Skills April 19, 2026 Exploring Natural Language Processing Techniques May 9, 2026 Understanding NLP Algorithms: The Masked Language Model January 24, 2026 The Role of an NLP Developer May 26, 2026 NLP vs. Machine Learning: What's the Difference? 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