# Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ

arXiv: https://arxiv.org/abs/1703.00565 · HTML: https://mlsanity.com/paper/1703.00565/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2017.

## Built on by (most cited first, 22 of 22)

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- [Predicting gender and age categories in English conversations using lexical, non-lexical, and turn-taking features](https://mlsanity.com/paper/2102.13355/) (2021)
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- [Automated clustering of COVID-19 anti-vaccine discourse on Twitter](https://mlsanity.com/paper/2203.01549/) (2022)
- [EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical and Life Science Text](https://mlsanity.com/paper/2304.07805/) (2023)
- [Signals from the Floods: AI-Driven Disaster Analysis through Multi-Source Data Fusion](https://mlsanity.com/paper/2505.17038/) (2025)
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- [Token Signatures of Code: Comparing Coding Behaviors Across Large Language Models](https://mlsanity.com/paper/2609.22097/) (2026)

## Abstract (arXiv)

Scattertext is an open source tool for visualizing linguistic variation between document categories in a language-independent way. The tool presents a scatterplot, where each axis corresponds to the rank-frequency a term occurs in a category of documents. Through a tie-breaking strategy, the tool is able to display thousands of visible term-representing points and find space to legibly label hundreds of them. Scattertext also lends itself to a query-based visualization of how the use of terms with similar embeddings differs between document categories, as well as a visualization for comparing the importance scores of bag-of-words features to univariate metrics.
