GlossaryAI search
Natural Language Processing
Natural language processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language in a meaningful way, making it foundational to how answer engines extract meaning from review text and synthesize customer opinions into summaries.
Also known as NLP
Natural language processing is the field of AI that lets computers work with human language. It underpins everything from sentiment analysis to question-answering systems. When you type a question into ChatGPT, Perplexity, or Google AI Overviews, NLP is what allows those systems to parse what you asked, search for relevant information, and construct a coherent answer in prose. When an answer engine reads your product reviews, NLP techniques are extracting entities (what is the review talking about), relationships (how does the reviewer feel about it), and meaning (what did they actually say).
For an ecommerce store, NLP matters because answer engines use NLP to find, understand, and cite your reviews. A corpus of reviews written in clear, specific language about concrete product features will be far easier for NLP systems to parse accurately than vague or contradictory text. If a hundred reviews say "runs small" in slightly different words, an NLP system has to recognize that these variations all point to the same issue. If they use slang, abbreviations, or context-dependent references, the system works harder and often fails. If the reviews are full of sarcasm ("oh great, another broken zipper"), standard NLP models will misread the tone.
NLP is also what powers the search inside your own store. When you add review search or filtering, you are relying on NLP (or at least tokenization and semantic search built on NLP foundations) to match what a customer types against the full text of your reviews. The clearer and more consistent your reviews are, the better that search works.
For review moderation and management, NLP tools can flag potential issues: fake or incentivized language, spam, or abuse. For routing, NLP can understand intent (a review complaining about shipping vs. praising quality) and direct escalations accordingly. The capability of these tools is limited by the same signal-to-noise problem that affects all NLP: if your review corpus is messy, the tools fail more often. If it is consistent and honest, they work better.