# Text Annotation

Source: https://www.potatoannotator.com/docs/guides/text-annotation

**Text annotation means labeling written language: sorting documents into categories, tagging the topics in an article, rating a passage for quality, or writing a correction. It is the most common annotation task in [natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing), and it is what Potato was first built for.** This guide covers the whole-document text tasks; for marking regions *inside* text, see [Span Annotation](/docs/guides/span-annotation).

## The text tasks at a glance

- **Document classification**: one label for the whole text ([text classification](https://en.wikipedia.org/wiki/Document_classification)).
- **Multi-label tagging**: several labels at once, such as topics or content warnings.
- **Rating and scoring**: a position on a scale, such as quality or [sentiment](https://en.wikipedia.org/wiki/Sentiment_analysis) intensity.
- **Free-text**: a written answer, paraphrase, or correction.

## Classification: one label per document

The workhorse of text annotation. Use `radio` when the categories are mutually exclusive:

```yaml
annotation_schemes:
  - annotation_type: radio
    name: sentiment
    description: "What is the overall sentiment of this review?"
    labels: [Positive, Negative, Neutral]
    sequential_key_binding: true
```

`sequential_key_binding` maps the labels to keys `1`, `2`, `3`, so annotators keep their hands on the keyboard. On a job of thousands of items this is a large speed-up. See the live [sentiment analysis](/showcase/sentiment-analysis) design for a working example.

## Multi-label: several tags at once

When more than one label can apply, use `multiselect`. Bound the selection count to match your guidelines:

```yaml
annotation_schemes:
  - annotation_type: multiselect
    name: content_warnings
    description: "Select every content warning that applies."
    labels: [Violence, Profanity, Sexual content, Self-harm, None]
    min_selections: 1
    max_selections: 5
```

Content moderation is a classic multi-label text task; the [toxicity detection](/showcase/toxicity-detection) design combines a category with a highlighted span.

## Rating text on a scale

To capture *degree* rather than category, use a [Likert scale](https://en.wikipedia.org/wiki/Likert_scale):

```yaml
annotation_schemes:
  - annotation_type: likert
    name: helpfulness
    description: "How helpful is this answer?"
    size: 5
    min_label: "Not helpful"
    max_label: "Very helpful"
```

See [Rating Scales](/docs/guides/rating-scales) for scale-design pitfalls such as acquiescence bias and how many points to use.

## Free-text and corrections

Sometimes the most useful label is a sentence the annotator writes, a justification, a rewrite, or a transcription. Combine it with a category and show it only when relevant:

```yaml
annotation_schemes:
  - annotation_type: radio
    name: factuality
    description: "Is the claim supported by the source?"
    labels: [Supported, Contradicted, Not enough info]
  - annotation_type: text
    name: evidence
    description: "Quote the sentence that supports your choice."
    label_requirement:
      required: false
```

## Getting consistent text labels

Text is ambiguous, so consistency comes from the surrounding process, not the interface:

1. Write tight [guidelines](/docs/guides/writing-annotation-guidelines) with a "can't tell" option.
2. Have multiple annotators overlap on the same items.
3. Track [inter-annotator agreement](/docs/guides/inter-annotator-agreement) and adjudicate disagreements.
4. Speed up large jobs with [LLM pre-annotation](/docs/guides/llm-pre-annotation) and verify the suggestions by hand.

## Further reading

- [Span Annotation](/docs/guides/span-annotation), marking regions inside text
- [Choosing an Annotation Scheme](/docs/guides/choosing-an-annotation-scheme)
- [Annotation Schemes reference](/docs/core-concepts/annotation-schemes)