Evidence has demonstrated the presence of similarities in language use across people with various mental health conditions. In this work we investigate these relationships both as described in literature and as a data analysis problem. We also introduce a novel transfer learning based approach that learns from linguistic feature spaces of previous conditions and predicts unknown ones. Our model achieves strong performance, with F1 scores of 0.75, 0.80, and 0.76 at detecting depression, stress, and suicidal ideation in a first-of-its-kind transfer task and offering promising evidence that language models can harness learned patterns from known mental health conditions to aid in their prediction of others that may lie latent.
CITATION STYLE
Aich, A., & Parde, N. (2022). Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses. In CLPsych 2022 - 8th Workshop on Computational Linguistics and Clinical Psychology, Proceedings (pp. 89–104). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.clpsych-1.8
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