Natural Language Processing (NLP)

Natural language processing (NLP) is the field of artificial intelligence concerned with getting computers to work with human language. It covers turning text into a form machines can process, analysing its structure and meaning, and generating new text. Search engines, chatbots, translation tools and the ML APIs SEOs use every day are all built on NLP.

How it works

Classic NLP processes text in stages, from structure to meaning:

• Pre-processing and lexical analysis: splitting text into sentences and tokens (tokenisation), removing stop words, and reducing words to base forms (stemming and lemmatisation).
• Syntax analysis: tagging parts of speech and parsing how words in a sentence relate.
• Semantic analysis: working out meaning, which includes entity extraction, sentiment analysis, classification and topic modelling.

Modern NLP relies on embeddings and transformer models such as BERT, and on large language models that can perform many of these tasks from a prompt. Purpose-built NLP APIs still return structured, repeatable outputs (types, salience, scores) that chatbots do not.

How it’s used in SEO and marketing

• Entity extraction and salience checks on pages and queries.
• Sentiment analysis of reviews, SERP titles and brand mentions.
• Text classification of pages, queries and support tickets.
• Keyword clustering and topic modelling.
• Syntax analysis to study how questions and headings are phrased.
• Generating and checking meta descriptions, summaries and alt text.
• Understanding search itself: query understanding, passage retrieval and AI answer generation are all NLP tasks, so explicit writing that names its entities and relationships is easier for both search engines and LLMs to interpret.

Example

A brand pastes 2,000 product reviews into MLforSEO’s no-code Sheets templates. The Natural Language API returns the entities customers mention (delivery, sizing, a product line) and the sentiment around each. Grouped in a dashboard, the results show that negative reviews cluster around sizing on one range, which becomes both a product fix and an FAQ content brief.

Limitations and things to watch

• Language models struggle with sarcasm, slang, domain jargon and very short text.
• Support varies by language; check that the API or model handles yours.
• Generative models can invent entities or facts. For repeatable analysis, prefer task-specific APIs or validate outputs.
• Results depend heavily on clean input. Strip navigation and boilerplate before analysing pages.

Learn it on MLforSEO

• What Entity Extraction Is, and Where It Fits in SEO
• How to do Syntax Analysis with Google’s Natural Language API in Google Sheets (Apps Script)
• Comparing NLP APIs for Entity and Sentiment Analysis (vs Generative AI)
• Complete Guide to Entity Extraction & Sentiment Analysis: Marketing and SEO Use Cases
• The Introduction to Machine Learning for SEO course