Cortext Manager Documentation

Welcome to the documentation of Cortext Manager, the data analysis platform for citizens and researchers in the social sciences and humanities.

Throughout these pages we will often refer to a corpus. By that we mean information on an ensemble of documents, containing for each document both its textual contents and its metadata. Metadata will vary from corpus to corpus, but usually contains information on the authors, dates, editors or locations pertaining to the document.

All the action in Cortext takes place within a project, and so you must start by creating one. Projects help you organize your work and share it with collaborators.

The first step to treat your corpus is to produce and upload it. You should then be led to parse it (the parsing script should be automatically launched after upload). This task will convert the original corpus into a format (an sqlite database) that other tasks in Cortext are prepared to use.

Once you have completed the data parsing step, different tools – we call them “scripts” or “Cortext methods” – are at your disposal to analyze your corpus. It all beings with choosing to start a script.

Start script form: select the script you wish to launch, then the corpus (.db file)

When starting a script, you will be asked to choose which script to start, and then a corpus to apply it to. The corpus should be one of the parsed databases (.db file) in your project. Then, a form in which to provide the parameters for the analysis will appear. Some parameters are required, while others have default values. After choosing the parameters, you may optionally label the analysis, and proceed to start the script.

Once a script is started, an entry for the new analysis will appear in the project page. Once it completes, the outputs are presented there. A log of its execution is also available, by clicking on the (green or red) flag. Should a script not reach completion, the last line of this log should provide some explanation to help you solve the issue or report it in our forum.

Cortext proposes a full ecosystem of modeling and exploratory tools for analyzing text corpora. The main menu of this documentation provides the list of available methods. As a user, you are free to define your own workflow of analysis.

A global schema with most methods provided by Cortext

The so called processing scripts are typically used to prepare the corpus for later analysis, when needed. They allow you to extract, transform and match information, such as to find the names of cities and convert those to coordinates, or search the text for names in a list you provide and index your documents with them. They can be categorized into Data Processing, Text Processing, Time Processing and Spatial Processing.

  • Lexical Extraction automatically extracts  list of pertinent terms using NLP technics
  • Named Entity Recognizer detects named entities such as persons, organizations, locations, etc
  • One can also indexes databases with their own custom terms list, a dedicated interface is proposed to easily create your own lists
  • Period Detector longitudinally analyzes the composition of your data to automatically detect  structurally distinct periods
  • You can customize the periods you wish to work on with  Period Slicer
  • Quantative data may also be very easily pre-processed with the Data Slicer script
  • Query A Corpus to create any sub-corpus resulting from a complex query
  • Different scripts allow to filter out and clean categorical lists: List Builder and Corpus List Indexer
  • Distant Reading builds an interface which allows to compare the dynamic profiles of words in a dynamic corpus
  • Geocode addresses and add to your corpus city names, region names, longitude and latitude coordinates;
  • Geospatial exploration tool to explore the geographical distribution of longitude and latitude coordinates across Urban and Rural Areas, NUTS and other spatial units.

An additional category for Data Exploration collects scripts that let you directly observe your corpus.

  • Demography will generate basic descriptive statistics about the structure and evolution of the main fields in your dataset
  • Distant Reading builds an interface which allows to compare the dynamic profiles of words in a dynamic corpus
  • Contrast Analysis is an exploratory tool allowing to visualize terms with are over/under-represented in a given sub-corpus
  • Word2Vec Explorer maps large number of words which positon has been trained using word2vec model

And finally, you’ll find the more advanced Data Analysis methods:

  • Heterogeneous Networks Mapping performs homogeneous or heterogeneous network analysis and produces intelligible and tunable representation of dynamics
  • SASHIMI combines robust statistical modeling and interactive maps to provide a hierarchical navigation of the full corpus and of the relationships between terms, documents, and metadata.
  • Topic Modeling offer a solution for analyzing the semantic structure of collections of texts
  • Contingency Matrix provide a direct visualization of existing correlations between distinct fields in your data
  • Correspondance Analysis script provide minimal facilities to perform a multiple correspondance analysis on any set of variables

Now that you’re familiarized with the general aspects of Cortext Manager, we hope you’ll go ahead and try it out.

Don’t hesitate to ask for advice on the forum. You’ll just be asked to register there, as currently you can’t simply use your Cortext Manager credentials. You may also like to watch our videos, for a guided tour, or visit the gallery page to discover some interesting uses of Cortext Manager.

If you want to play around but can’t think of any dataset readily available, feel free to use this dataset of recipes from a old Kaggle competition. It features a set of almost 40,000 cooking recipes, along with their regional cuisine of origin as metadata. Upload the zip file, parse the corpus by declaring a json file in the parameters, and start exploring. You may also want to try this corpus compiling every State of the Union address since 1790, with discourses divided in paragraphs, and the speaker and year of address included as metadata.

500 most popular ingredients plotted
Recipes: network map with the 500 most popular ingredients (click on the image to interact)

In addition to this website, we regularly organize workshops and some material is available in this repository.

An additional deck of documents is found in our training materials section.

Latest documented scripts

Upload resource

Upload resource

You can upload any kind of documents (doc, pdf, power point) into your project. This is particularly useful for sharing these documents with the participants you are working with in the project or for exemple to store in your project a scientific article which would be usefull for your analysis. But you may want to ...


SASHIMI lets you study a corpus by simultaneously clustering documents and terms, revealing relationships between these clusters, and optionally extending them by clustering any categorical metadata dimension. It does so in a parsimonious and nested (hierarchical) fashion, based on robust, state-of-the-art statistical models that avoid displaying statistically insignificant structures and provide information at different scales ...


The profiling script offers very similar analytical capacities than  contingency analysis.  Given two fields of interest, it will consider a target entity in the first field and produce a visualization of how biased the entities of the second field are distributed in documents which have been tagged by this  target entity. For instance, one may ...
Visualise and explore the geographical distribution of your dataset across urban and rural areas

Visualise and explore the geographical distribution of your dataset across urban and rural areas

Geographical phenomena are by nature distributed. To deliver geographical coordinate to an address is one thing, but for the purpose of spatial analysis it is often necessary to aggregate geographical information, and associated data, on a more suitable scale. It is the case, for example, for analysing inter-urban or regional spatial dynamics based on human ...
Geocode your addresses and enrich your dataset with geographical information

Geocode your addresses and enrich your dataset with geographical information

In digitalized documents, especially those with texts, more than 70% contains geographical information. Nevertheless, a large proportion of this information are not formalized to be projected and manipulated on maps, and there are stored in various forms (Hill, 2006): pictures of places, toponyms in full text documents, addresses, structured metadata or, finally, geographical coordinates. Adding ...
Period Slicer

Period Slicer

Period Slicer allows users to customize the time periods according to which the corpus is analyzed. Parameters When launching Period Slicer, you need to input the different time periods which will be used to split the corpus. Each time period should be separated by semi-colons. Lists of values A time period is defined by a ...