Ríssola EA, Ramírez-Cifuentes D, Freire A, Crestani F. Suicide Risk Assessment on Social Media:USI-UPF at the CLPsych 2019 Shared Task. Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).
List of publications acknowledging the funding in Scopus.
The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository as well as at the UPF e-repository
Ríssola EA, Ramírez-Cifuentes D, Freire A, Crestani F. Suicide Risk Assessment on Social Media:USI-UPF at the CLPsych 2019 Shared Task. Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
Ríssola EA, Ramírez-Cifuentes D, Freire A, Crestani F. Suicide Risk Assessment on Social Media:USI-UPF at the CLPsych 2019 Shared Task. Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
This paper describes the participation of the USI-UPF team at the shared task of the 2019Computational Linguistics and Clinical Psychology Workshop (CLPsych2019). The goal is to assess the degree of suicide risk of social media users given a labelled dataset with their posts. An appropriate suicide risk assessment, with the usage of automated methods, can assist experts on the detection of people at risk and eventually contribute to prevent suicide. We propose a set of machine learning models with features based on lexicons, word embeddings, word level n-grams, and statistics extracted from users’ posts. The results show that the most effective models for the tasks are obtained integrating lexicon-based features, a selected set of n-grams, and statistical measures.