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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Beautiful visualizations of how language differs among document types.
| Date | Stars |
|---|---|
| 2026-07-24 | 2338 |
| 2026-07-25 | 2338 |
| 2026-07-28 | 2338 |
| 2026-07-30 | 2338 |
| 2026-08-09 | 2341 |
| 2026-08-17 | 2342 |
| 2026-08-21 | 2341 |
| 2026-09-03 | 2342 |
| 2026-09-07 | 2342 |
| 2026-09-09 | 2343 |
| 2026-09-11 | 2344 |
| 2026-09-12 | 2343 |
| 2026-09-16 | 2343 |
| 2026-09-20 | 2343 |
Today
— stars today
This week
— stars this week
This month
+2 stars this month
Momentum
0.0
growth rate 0.00%/day
[](https://travis-ci.org/JasonKessler/scattertext)
[]()
[](https://gitter.im/scattertext/Lobby)
[](https://twitter.com/jasonkessler)
# Scattertext 0.2.3
A tool for finding distinguishing terms in corpora and displaying them in an
interactive HTML scatter plot. Points corresponding to terms are selectively labeled
so that they don't overlap with other labels or points.
Cite as: Jason S. Kessler. Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ. ACL System
Demonstrations. 2017.
Below is an example of using Scattertext to visualize terms used in 2012 American
political conventions. The 2,000 most party-associated uni grams are displayed as
points in the scatter plot. Their x- and y- axes are the dense ranks of their usage by
Republican and Democratic speakers respectively.
```pydocstring
import scattertext as st
df = st.SampleCorpora.ConventionData2012.get_data().assign(
parse=lambda df: df.text.apply(st.whitespace_nlp_with_sentences)
)
corpus = st.CorpusFromParsedDocuments(
df, category_col='party', parsed_col='parse'
).build().get_unigram_corpus().compact(st.AssociationCompactor(2000))
html = st.produce_scattertext_explorer(
corpus,
category='democrat',
category_name='Democratic',
not_category_name='Republican',
minimum_term_frequency=0,
pmi_threshold_coefficient=0,
width_in_pixels=1000,
metadata=corpus.get_df()['speaker'],
transform=st.Scalers.dense_rank,
include_gradient=True,
left_gradient_term='More Republican',
middle_gradient_term='Metric: Dense Rank Difference',
right_gradient_term='More Democratic',
enable_zoom=True,
)
open('./demo_compact.html', 'w').write(html)
```
The HTML file written would look like the image below. Click on it for the actual interactive visualization. With
`enable_zoom=True`, the scatter plot supports mouse-wheel or trackpad zooming and drag panning.
[](https://jasonkessler.github.io/demo_compact.html)
## Citation
Jason S. Kessler. Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ. ACL System Demonstrations. 2017.
Link to paper: [arxiv.org/abs/1703.00565](https://arxiv.org/abs/1703.00565)
```
@article{kessler2017scattertext,
author = {Kessler, Jason S.},
title = {Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ},
booktitle = {Proceedings of ACL-2017 System Demonstrations},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics},
}
```
**Table of Contents**
- [Installation](#installation)
- [Overview](#overview)
- [Customizing the Visualization and Plotting Dispersion](#customizing-the-visualization-and-plotting-dispersion)
- [Tutorial](#tutorial)
- [Help! I don't know Python but I still want to use Scattertext](#help-i-dont-know-python-but-i-still-want-to-use-scattertext)
- [Using Scattertext as a text analysis library: finding characteristic terms and their associations](#using-scattertext-as-a-text-analysis-library-finding-characteristic-terms-and-their-associations)
- [Visualizing term associations](#visualizing-term-associations)
- [Visualizing phrase associations](#visualizing-phrase-associations)
- [Adding color gradients to explain scores](#adding-color-gradients-to-explain-scores)
- [Visualizing Empath topics and categories](#visualizing-empath-topics-and-categories)
- [Visualizing the Moral Foundations 2.0 Dictionary](#visualizing-the-moral-foundations-2.0-dictionary)
- [Ordering Terms by Corpus Characteristicness](#ordering-terms-by-corpuExcerpt of 156,804 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2816890d24dfe1b2, topic:nlp, topic:natural-language-processing