Natural Language Processing (NLP)

Natural Language Processing (NLP) is the field of AI focused on enabling software to understand and produce human language across tasks like classification, extraction, and translation.

Also known as: NLP, computational linguistics, language AI

Natural Language Processing (NLP) is the field of AI focused on enabling software to understand and produce human language. It covers tasks like classifying text, extracting key information, detecting sentiment, translating, and summarizing across many languages and domains. NLP has existed for decades; what changed recently is the accuracy and ease of use brought by large language models.

What Natural Language Processing Means

Natural Language Processing is the branch of AI that helps computers read, interpret, and work with human language in text or speech. It includes classification tasks like sorting emails, extraction tasks like pulling key fields from a document, sentiment analysis, translation, summarization, and question answering. For marketers, NLP turns unstructured text such as reviews, support tickets, and survey responses into usable insight. It also powers many tools they already use, from email spam filters to search engines to social listening platforms, often invisibly underneath the interface. The field is broader than large language models, even though LLMs have become the most visible application of NLP in marketing today.

How Natural Language Processing Works

A Natural Language Processing system converts language into structured representations a model can analyze, then applies statistical or machine learning methods to find patterns. Classical NLP uses techniques like tokenization, part-of-speech tagging, and named entity recognition, often with lighter machine learning models trained for specific tasks. Modern NLP increasingly uses large language models that can handle multiple tasks in one system, though lighter methods are still appropriate for simple, high-volume work. Marketing applications include sentiment analysis on social posts, automated tagging of content, keyword extraction from interviews, and analysis of open-ended survey responses. The choice of method depends on accuracy needs, cost, and volume, with the cheaper classical methods often appropriate for routine work.

Common Pitfalls and Misconceptions

A common misconception is that Natural Language Processing is new; it has existed for decades. What changed recently is the accuracy and ease of use brought by large language models, which made many previously hard tasks reliable enough for production. Another pitfall is treating every NLP task as a job for an LLM when lighter methods would be cheaper, faster, and just as accurate for simple classification. A third is overestimating accuracy on nuanced language; NLP can misread sarcasm, irony, context, and industry jargon, so results are best used as directional signals rather than verdicts on individual messages.

Natural Language Processing in Practice

The practitioner point is that Natural Language Processing is now embedded in so many marketing tools that most teams use it without knowing. The useful question is not whether to adopt NLP but where the team’s data still lives outside it. Unstructured text in support tickets, sales call notes, and review platforms often holds the strongest voice-of-customer signal in the business, and tools that bring NLP to that data tend to unlock more value than another lead-scoring model on the same fields the team already uses. The entry point is also far lower than it used to be, since many platforms include built-in NLP features that work without coding.

Back to the Glossary

Common questions.

How do marketers use NLP day to day?

NLP underpins sentiment analysis, social listening, automated tagging of content, keyword extraction, and the analysis of open-ended survey responses. It helps teams understand large volumes of customer language without reading every comment manually, which is where the real leverage lives.

Is NLP the same as a large language model?

No. NLP is the broad field; large language models are one powerful approach within it. Many NLP tasks, such as simple text classification, can still be done with lighter, faster methods that do not require a large model and are cheaper to run at scale.

What is sentiment analysis?

Sentiment analysis is an NLP task that judges whether text expresses a positive, negative, or neutral attitude. Marketers use it to gauge reactions to campaigns, monitor brand perception, and prioritize support issues, though sarcasm and context can still trip it up in real-world data.

How is NLP different from generative AI?

NLP is the broad field of helping computers work with human language, including tasks like classification, extraction, and sentiment analysis. Generative AI focused on text is one powerful application within NLP. Many NLP tasks do not generate new content at all, so the two overlap but are not the same.

What are the limits of NLP for marketers?

NLP can misread sarcasm, irony, context, and nuance, and accuracy depends on the quality and language of the data. Results from tasks like sentiment analysis are directional, so they are best used to spot trends and prioritize, not as precise verdicts on individual messages.

Where is NLP underused in B2B marketing?

Most teams have rich unstructured data in sales call notes, support tickets, and review platforms that never gets analyzed systematically. Bringing NLP to those sources often surfaces stronger voice-of-customer signal than yet another quantitative model on the data the team already uses.

Do you need a data team to use NLP?

Not for most use cases. Many marketing platforms include built-in NLP features that work without coding, and standalone tools handle common tasks like sentiment analysis and topic detection out of the box. Custom NLP development still benefits from a data team, but the entry point is much lower than it used to be.

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