Generating Navigable Semantic Maps from Social Sciences Corpora

TL;DR

This paper presents methods to extract and explore socio-semantic networks from social sciences texts, enabling better understanding of social facts and idea evolution through structured maps and interactive tools.

Contribution

It introduces new NLP techniques for automatic information extraction from social science texts and innovative tools for dynamic exploration of socio-semantic networks.

Findings

Effective extraction of relevant social science information from texts.

Development of interactive tools for network navigation and analysis.

Enhanced understanding of social facts and idea evolution.

Abstract

It is now commonplace to observe that we are facing a deluge of online information. Researchers have of course long acknowledged the potential value of this information since digital traces make it possible to directly observe, describe and analyze social facts, and above all the co-evolution of ideas and communities over time. However, most online information is expressed through text, which means it is not directly usable by machines, since computers require structured, organized and typed information in order to be able to manipulate it. Our goal is thus twofold: 1. Provide new natural language processing techniques aiming at automatically extracting relevant information from texts, especially in the context of social sciences, and connect these pieces of information so as to obtain relevant socio-semantic networks; 2. Provide new ways of exploring these socio-semantic networks, thanks to tools allowing one to dynamically navigate these networks, de-construct and re-construct them interactively, from different points of view following the needs expressed by domain experts.