Work

WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data

Year: 2024

Type: software-paper

Abstract: We present WordGraph, a Python package for exploring the topics of documents corpora. WordGraph provides causal graphical models from text data vocabulary and proposes interactive visualizations of terms networks. Our ease-to-use package is provided with a pre-built pipeline to access the main modules through jupyter widgets. It results in the encapsulation of a whole vocabulary exploration process within a single jupyter notebook cell, with straightforward parameters settings and interactive plots. WordGraph pipeline is fully customizable by adding/removing widgets or changing default parameters. To assist users with no background in Python nor jupyter notebook, but willing to explore large corpora topics, we also propose an automatic dashboard generation from the customizable jupyter notebook pipeline in a web application style. WordGraph is available through a GitHub repository at https://github.com/MLDS-software/WordGraph.

Source: ACM International Conference on Web Search and Data Mining (WSDM)

DOI: 10.1145/3616855.3635698

Authors: Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif

Institutions: Université Paris Cité, Centre Borelli

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Awards: Learning causal effects between phenome and exposome from large amounts of heterogeneous data in human complex diseases, G3010527862