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A multitask framework to detect depression, sentiment and multi-label emotion from suicide notes - 2021

Research Area:  Machine Learning


The significant rise in suicides is a major cause of concern in public health domain. Depression plays a major role in increasing suicide ideation among the individuals. Although most of the suicides can be avoided with prompt intercession and early diagnosis, it has been a serious challenge to detect the at-risk individuals. Our current work focuses on learning three closely related tasks, viz. depression detection, sentiment citation, and to investigate their impact in analysing the mental state of the victims. We extend the existing standard emotion annotated corpus of suicide notes in English, CEASE, with additional 2539 sentences collected from 120 new notes. We annotate the consolidated corpus with appropriate depression labels and multi-label emotion classes. We further leverage weak supervision to annotate the corpus with sentiment labels. We propose a deep multitask framework that features a knowledge module that uses SenticNets IsaCore and AffectiveSpace vector-spaces to infuse external knowledge specific features into the learning process. The system models emotion recognition (the primary task), depression detection and sentiment classification (the secondary tasks) simultaneously. Experiments show that our proposed multitask system obtains the highest cross-validation MR of 56.47 %. Evaluation results show that all our multitask models perform better than their single-task variants indicating that the secondary tasks (depression detection and sentiment classification) improve the performance of the primary task (emotion recognition) when all tasks are learned jointly.

Author(s) Name:  Soumitra Ghosh, Asif Ekbal & Pushpak Bhattacharyya

Journal name:  Cognitive Computation

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s12559-021-09828-7

Volume Information:  volume 14, pages 110–129 (2022)