Generating Navigable Semantic Maps from Social Sciences Corpora
This addresses the challenge of making unstructured online text usable for social science research, though it appears incremental as it builds on existing NLP and network analysis methods.
The paper tackles the problem of extracting structured information from social sciences text corpora to create navigable socio-semantic networks, resulting in new NLP techniques and interactive exploration tools for domain experts.
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.