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3 septembre 2021
- doi: 10.4000/books.aaccademia.8305
- eisbn: 9791280136336
https://www.openedition.org/12554 , info:eu-repo/semantics/openAccess
Mots-clés
Computational Linguistics Fine-grained sentiment analysis Distributional Semantics Quantitative Linguistic Investigations Gender Bias Depression from Social Media Online Hate Speech Automatic Sarcasm Detection TrAVaSI AriEmozione AEREST COVID-19 Linguistic Ostracism in Social Networks Multilingual NLU E3C Project DistilBERT Twitter during Pandemic LAN000000 Linguistics CBXSujets proches
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Ana-Maria Bucur et al., « Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores », Accademia University Press, ID : 10.4000/books.aaccademia.8305
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Résumé
Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology. A crucial aspect of this problem is prevention and early diagnosis, as suicide resulted from depression being the second leading cause of death for young adults. In this work, we focus on methods for detecting the early onset of depression from social media texts, in particular from Reddit. To that end, we explore the eRisk 2018 dataset and achieve good results with regard to the state of the art by leveraging topic analysis and learned confidence scores to guide the decision process.