---
name: natural-language-understanding-index
title: About
description: With IBM Watson® Natural Language Understanding, developers can analyze semantic features of text input, including categories, concepts, emotion, entities, keywords, metadata, relations, semantic roles, and sentiment.
last-updated: 2024-10-17
---

> ## Documentation Index
> The table of contents for this documentation set is at https://cloud.ibm.com/docs/natural-language-understanding?format=markdown
> The index for all IBM Cloud docs is at: https://cloud.ibm.com/docs/llms.txt
> Use these files to discover more information as needed.

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# About
{: #about}

With IBM Watson® Natural Language Understanding, developers can analyze semantic features of text input, including categories, concepts, emotion, entities, keywords, metadata, relations, semantic roles, and sentiment.
{: shortdesc}

## Features
{: #features}

Send requests to the API with text, HTML, or a public URL, and specify one or more of the following features to analyze:

### Categories
{: #categories}

Categorize your content using a five-level classification hierarchy. View the complete list of categories [here](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-categories-hierarchy&format=markdown). For example:

**Input**
> url: "www.cnn.com"

**Response**
> /news </br>
> /art and entertainment </br>
> /movies and tv/television </br>
> /news </br>
> /international news

### Concepts
{: #concepts}

Identify high-level concepts that aren't necessarily directly referenced in the text. For example:

**Input**
> text: "Natural Language Understanding uses natural language processing to analyze text."

**Response**
> Linguistics </br>
> Natural language processing </br>
> Natural language understanding

### Emotion
{: #emotion}

Analyze emotion conveyed by specific target phrases or by the document as a whole. You can also enable emotion analysis for entities and keywords that are automatically detected by the service. For example:

**Input**
> text: "I love apples, but I hate oranges." </br>
> targets: "apples", and "oranges"

**Response**
> "apples": joy </br>
> "oranges": anger

### Entities
{: #entities}

Find people, places, events, and other types of entities mentioned in your content. View the complete list of entity types and subtypes [here](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-entity-type-systems&format=markdown). For example:

**Input**
> text: "IBM is an American multinational technology company headquartered in Armonk, New York, United States, with operations in over 170 countries."

**Response**
> IBM: Company </br>
> Armonk: Location </br>
> New York: Location </br>
> United States: Location

### Keywords
{: #keywords}

Search your content for relevant keywords. For example:

**Input**
>url: "[http://www-03.ibm.com/press/us/en/pressrelease/51493.wss](http://www-03.ibm.com/press/us/en/pressrelease/51493.wss)"

**Response**
>Australian Open </br>
>Tennis Australia </br>
>IBM SlamTracker analytics

### Metadata
{: #metadata}

For HTML and URL input, get the author of the webpage, the page title, and the publication date. For example:

**Input**
>url: "https://www.ibm.com/blogs/think/2017/01/cognitive-grid/"

**Response**
>Author: Stephen Callahan </br>
>Title: Girding the Grid with Cognitive Computing - THINK Blog </br>
>Publication date: January 31, 2017

### Relations
{: #relations}

Recognize when two entities are related, and identify the type of relation. For example:

**Input**
>text: "The Nobel Prize in Physics 1921 was awarded to Albert Einstein."

**Response**
>"awardedTo" relation between "Noble Prize in Physics" and "Albert Einstein" </br>
>"timeOf" relation between "1921" and "awarded"

### Semantic Roles
{: #semantic-roles}

Parse sentences into subject-action-object form, and identify entities and keywords that are subjects or objects of an action. For example:

**Input**
>text: "In 2011, Watson competed on Jeopardy!"

**Response**
>Subject: Watson </br>
>Action: competed </br>
>Object: on Jeopardy

### Sentiment
{: #sentiment}

Analyze the sentiment toward specific target phrases and the sentiment of the document as a whole. You can also get sentiment information for detected entities and keywords by enabling the sentiment option for those features. For example:

**Input**
>text: "Thank you and have a nice day!"

**Response**
>Positive sentiment (score: 0.91)

### Syntax
{: #syntax}

Identify the sentences and tokens in your text. For example:

**Input**
>text: "I love apples! I do not like oranges."

**Response**


|  Sentence | Location | 
| --- | --- |
| "I love apples!" | `[0, 14]` |
| "I do not like oranges." | `[15,37]` |

<br>

|  Token    | Lemma    | Part of Speech | Location   | 
|-----------|----------|----------------|------------|
| "I"       | "I"      | `PRON`         | `[0, 1]`   |
| "love"    | "love"   | `VERB`         | `[2, 6]`   |
| "apples"  | "apple"  | `NOUN`         | `[7, 13]`  |
| "!"       |          | `PUNCT`        | `[13, 14]` |
| "I"       |  "I"     | `PRON`         | `[15, 16]` |
| "do"      | "do"     | `AUX`          | `[17, 19]` |
| "not"     | "not"    | `PART`         | `[20, 23]` |
| "like"    | "like"   | `VERB`         | `[24, 28]` |
| "oranges" | "orange" | `NOUN`         | `[29, 36]` |
| "."       |          | `NOUN`         | `[36, 37]` |

## Supported languages
{: #supported-languages}

See the [Language support documentation](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-language-support&format=markdown) for details about supported languages in Natural Language Understanding.