Abstract : Combining linguistic
data and behavioral sciences, we use NLP to implement machine learning
and inspect social media in a suicide surveillance system. We have
conducted prospective studies to understand the linguistic expressions,
and discourse features of suicidal subjects. The goal of this research
was to build a machine learning processing using the linguistic
characteristics. To achieve this, we applied machine learning
classifiers on linguistic data captured from heterogeneous sources
(blogs, websites, forums, social networks, etc.). The captured data were
then used for training machine on information extraction in order to
identify linguistic markers of suicide. In this paper, we provide an
overview of the automated tracking and monitoring system for suicidal
ideation and risk, which draws on predictive linguistics methods and
techniques, based on a large sample of suicide messages posted online.
https://www.hal.inserm.fr/inserm-02521389
Contributeur : Mathieu Guidere <mathieu.guidere@inserm.fr>
Soumis le : vendredi 27 mars 2020 - 13:26:09
Dernière modification le : dimanche 29 mars 2020 - 01:40:51
Contributeur : Mathieu Guidere <mathieu.guidere@inserm.fr>
Soumis le : vendredi 27 mars 2020 - 13:26:09
Dernière modification le : dimanche 29 mars 2020 - 01:40:51
