scattertext | Beautiful visualizations of how language differs | Natural Language Processing library

scattertext is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Natural Language Processing applications. scattertext has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install scattertext' or download it from GitHub, PyPI.

This is a tool that's intended for visualizing what words and phrases are more characteristic of a category than others. Consider the example at the top of the page. Looking at this seem overwhelming. In fact, it's a relatively simple visualization of word use during the 2012 political convention. Each dot corresponds to a word or phrase mentioned by Republicans or Democrats during their conventions. The closer a dot is to the top of the plot, the more frequently it was used by Democrats. The further right a dot, the more that word or phrase was used by Republicans. Words frequently used by both parties, like "of" and "the" and even "Mitt" tend to occur in the upper-right-hand corner. Although very low frequency words have been hidden to preserve computing resources, a word that neither party used, like "giraffe" would be in the bottom-left-hand corner. The interesting things happen close to the upper-left and lower-right corners. In the upper-left corner, words like "auto" (as in auto bailout) and "millionaires" are frequently used by Democrats but infrequently or never used by Republicans. Likewise, terms frequently used by Republicans and infrequently by Democrats occupy the bottom-right corner. These include "big government" and "olympics", referring to the Salt Lake City Olympics in which Gov. Romney was involved. Terms are colored by their association. Those that are more associated with Democrats are blue, and those more associated with Republicans red. Terms that are most characteristic of the both sets of documents are displayed on the far-right of the visualization. The inspiration for this visualization came from Dataclysm (Rudder, 2014). Scattertext is designed to help you build these graphs and efficiently label points on them. The documentation (including this readme) is a work in progress. Please see the tutorial below as well as the PyData 2017 Tutorial. Poking around the code and tests should give you a good idea of how things work. The library covers some novel and effective term-importance formulas, including Scaled F-Score.