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Scattertext

Scattertext is a Python library that finds terms distinguishing categories in text corpora and presents them in interactive HTML scatter plots.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Scattertext — OpenViST project profile in AI Data & Analytics.
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Understand the project

Practical overview

When it fits

Use Scattertext when you have text documents assigned to categories and want to inspect which terms distinguish those groups rather than relying only on a summary of overall word frequencies.

Who it is for

  • Researchers comparing language across groups of documents
  • Analysts exploring category-specific vocabulary in text corpora
  • Python users who want to create interactive, browser-viewable term visualizations

Documented uses

  • Identify words or phrases that are more characteristic of one document category than another.
  • Compare term usage across two groups using a scatter plot.
  • Save an interactive visualization as an HTML file for browser viewing.
Before you choose

Requirements & limits

  • The workflow requires Python and a corpus with document categories.
  • Python 3.11 or higher is specified in the installation instructions.
  • The project offers a whitespace NLP alternative to spaCy, but documents limitations for that option.
  • The output is an HTML file, with Chrome and Safari identified as the browsers where it looks best.
  • The repository lists the Apache-2.0 license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Create an interactive comparison from a parsed corpus

Build a category-based term comparison and save its interactive scatter plot as an HTML file.

Before you start

  • Python 3.11 or higher
  • Install Scattertext with pip
  • A dataframe containing document text, parsed documents, and a category column

Steps

  1. Install Scattertext with the command below.

    Documented command
    pip install scattertext

  2. Prepare parsed documents and provide the category column when building a corpus.

  3. Use produce_scattertext_explorer with a category and comparison labels to create the HTML output.

  4. Write the returned HTML string to a local file.

Expected outcome

A local interactive HTML scatter plot displaying terms associated with the selected category and its comparison group.

Guide limitations

  • The README recommends additional packages for full functionality.
  • The whitespace NLP alternative is not compatible with word_similarity_explorer and has lower-performance tokenization and sentence-boundary detection.
  • The documentation says the HTML output looks best in Chrome and Safari.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
JasonKessler/scattertext · Official homepage title · retained in its source language
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