Citation

Ghosh S, Ekbal A, Bhattacharyya P. Cogn. Comput. 2022; 14(1): 110-129.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12559-021-09828-7

PMID

unavailable

Abstract

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 SenticNet's 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. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Language: en

Keywords

Diagnosis; Suicide notes; Emotion recognition; Depression detection; Speech recognition; Vector spaces; Cross validation; Deep multitask learning; Evaluation results; External knowledge; Fine-grained emotion recognition; Knowledge modules; Learning process; Multi-task model; Sentiment classification