---
title: Text analysis
description: Text analysis is the process of converting unstructured text, like the body of an email or a product description, into a structured format that’s optimized...
url: https://www.elastic.co/elastic/docs-builder/docs/3028/manage-data/data-store/text-analysis
products:
  - Elasticsearch
applies_to:
  - Elastic Cloud Serverless: Generally available
  - Elastic Stack: Generally available
---

# Text analysis
_Text analysis_ is the process of converting unstructured text, like the body of an email or a product description, into a structured format that’s [optimized for search](https://www.elastic.co/elastic/docs-builder/docs/3028/solutions/search/full-text).
Text analysis enables Elasticsearch to perform full-text search, where the search returns all *relevant* results rather than only exact matches. For example, if you search for `Quick fox jumps`, you probably want the document that contains `A quick brown fox jumps over the lazy dog`, and you might also want documents that contain related words like `fast fox` or `foxes leap`.
Elasticsearch performs text analysis when indexing or searching [`text`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3028/reference/elasticsearch/mapping-reference/text) fields. If your index does _not_ contain `text` fields, no further setup is needed; you can skip the pages in this section. If you _do_ use `text` fields or your text searches aren’t returning results as expected, configuring text analysis can often help. You should also look into analysis configuration if you’re using Elasticsearch to:
- Build a search engine
- Mine unstructured data
- Fine-tune search for a specific language
- Perform lexicographic or linguistic research


## Tokenization

Analysis makes full-text search possible through *tokenization*: breaking a text down into smaller chunks, called *tokens*. In most cases, these tokens are individual words.
If you index the phrase `the quick brown fox jumps` as a single string and the user searches for `quick fox`, it isn’t considered a match. However, if you tokenize the phrase and index each word separately, the terms in the query string can be looked up individually. This means they can be matched by searches for `quick fox`, `fox brown`, or other variations.

## Normalization

Tokenization enables matching on individual terms, but each token is still matched literally. This means:
- A search for `Quick` would not match `quick`, even though you likely want either term to match the other
- Although `fox` and `foxes` share the same root word, a search for `foxes` would not match `fox` or vice versa.
- A search for `jumps` would not match `leaps`. While they don’t share a root word, they are synonyms and have a similar meaning.

To solve these problems, text analysis can *normalize* these tokens into a standard format. This allows you to match tokens that are not exactly the same as the search terms, but similar enough to still be relevant. For example:
- `Quick` can be lowercased: `quick`.
- `foxes` can be *stemmed*, or reduced to its root word: `fox`.
- `jump` and `leap` are synonyms and can be indexed as a single word: `jump`.

To ensure search terms match these words as intended, you can apply the same tokenization and normalization rules to the query string. For example, a search for `Foxes leap` can be normalized to a search for `fox jump`.

## Customize text analysis

Text analysis is performed by an [*analyzer*](https://www.elastic.co/elastic/docs-builder/docs/3028/manage-data/data-store/text-analysis/anatomy-of-an-analyzer), a set of rules that govern the entire process.
Elasticsearch includes a default analyzer, called the [standard analyzer](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3028/reference/text-analysis/analysis-standard-analyzer), which works well for most use cases right out of the box.
If you want to tailor your search experience, you can choose a different [built-in analyzer](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3028/reference/text-analysis/analyzer-reference) or even [configure a custom one](https://www.elastic.co/elastic/docs-builder/docs/3028/manage-data/data-store/text-analysis/create-custom-analyzer). A custom analyzer gives you control over each step of the analysis process, including:
- Changes to the text *before* tokenization
- How text is converted to tokens
- Normalization changes made to tokens before indexing or search