Gargantext is a collaborative web platform for the exploration of sets of unstructured documents. It combines tools from natural language processing, text-mining, complex networks analysis and interactive data visualization to pave the way toward new kinds of interactions with your digital corpora.
You will not find this software very useful without also running or being granted access to a backend.
This software is free software, developed by the CNRS Complex Systems Institute of Paris Île-de-France (ISC-PIF) and its partners.
The build requires the following system dependencies preinstalled:
- NodeJS (11+)
- Yarn (Recent)
On debian testing, debian unstable or ubuntu:
sudo apt update && sudo apt install nodejs yarn
On debian stable:
curl -sL https://deb.nodesource.com/setup_11.x | sudo bash -
sudo apt update && sudo apt install nodejs
On Mac OS X with homebrew:
brew install node
For other platforms, please refer to the nodejs website.
On debian or ubuntu:
curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -
echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list
sudo apt update && sudo apt install yarn
On Mac OS X with homebrew:
brew install yarn
For other platforms, please refer to the yarn website.
Once you have node and yarn installed, you may install deps with:
yarn install -D && yarn install-ps
You will likely want to check your work in a browser. We provide a local development webserver that serves on port 5000 for this purpose:
yarn server
To generate a new browser bundle to test:
yarn build
If you are rapidly iterating and just want to type check your code:
yarn compile
You may access a purescript repl if you want to explore:
yarn repl
If you need to reinstall dependencies such as after a git pull or branch switch:
yarn install -D && yarn install-ps # both javascript and purescript
If something goes wrong building after a deps update, you may clean build artifacts and try again:
yarn clean-js # clean javascript, very useful
yarn clean-ps # clean purescript, should never be required, possible purescript bug
yarn clean # clean both purescript and javascript
If you edit the SASS, you'll need to rebuild the CSS:
yarn sass
A guide to getting set up with the IDE integration is coming soon, I hope. of this document.
Please follow CONTRIBUTING.md
Edit Config.purs
. Find the function endConfig'
just after the
imports and edit back
. The definitions are not far below, just after
the definitions of the various front
options.
Example (using demo.gargantext.org
as backend):
endConfig' :: ApiVersion -> EndConfig
endConfig' v = { front : frontRelative
, back : backDemo v }
Add it to package.json
, under dependencies
if it is needed at
runtime or devDependencies
if it is not.
Add it to psc-package.json
without the purescript-
prefix.
If is not in the package set, you will need to read the next section.
You need to add an entry to the relevant map in
packages.dhall
. There are comments in the file explaining how it
works. It's written in dhall, so you can use comments and such.
You will then need to rebuild the package set:
yarn rebuild-set
yarn rebase-set && yarn rebuild-set
This will occasionally result in swearing when you go on to build.
Making sense of out text isn't actually that hard, but it does require a little background knowledge to understand.
N-grams are at the heart of how Gargantext makes sense out of text.
There are two common meanings in the literature for n-gram:
- a sequence of
n
characters - a sequence of
n
words
Gargantext is focused on words. Here are some example word n-grams;
coffee
(unigram or 1-gram)need coffee
(bigram or 2-gram)one coffee please
(trigram or 3-gram)here is your coffee
(4-gram)i need some more coffee
(5-gram)
N-grams are matched case insensitively and across whole words. Examples:
Text | N-gram | Matches |
---|---|---|
Coffee cup |
coffee |
YES |
Coffee cup |
off |
NO, not a whole word |
Coffee cup |
coffee cup |
YES |
You may read more about n-grams on wikipedia.
Gargantext allows you to define n-grams interactively in your browser and explore the relationships they uncover across a corpus of text.
Various metrics can be applied to n-grams, the most common of which is the number of times an n-gram appears in a document.
document
: One or more texts comprising a single logical document
field
: A portion of a document, e.g. title
, abstract
, body
corpus
: A collection of documents
n-gram/ngram
: A word or words to be indexed, consisting of n
words.
This technically includes skip-grams, but in the general case
the words will be contiguous.
unigram/1-gram
: A one-word n-gram, e.g. cow
, coffee
bigram/2-gram
: A two-word n-gram, e.g. coffee cup
trigram/3-gram
: A three-word n-gram, e.g. coffee cup holder
skip-gram
: An n-gram where the words are not all adjacent. Not yet supported.
k-skip-n-gram
: An n-gram where the words are at most distance k from each other.