Deep cascaded multitask framework for detection of temporal orientation, sentiment and emotion from suicide notes

With the upsurge in suicide rates worldwide, timely identification of the at-risk individuals using computational methods has been a severe challenge. Anyone presenting with suicidal thoughts, mainly recurring and containing a deep desire to die, requires urgent and ongoing psychiatric treatment. This work focuses on investigating the role of temporal orientation and sentiment classification (auxiliary tasks) in jointly analyzing the victims' emotional state (primary task). Our model leverages the effectiveness of multitask learning by sharing features among the tasks through a novel multi-layer cascaded shared-private attentive network. We conducted our experiments on a diversified version of the prevailing standard emotion annotated corpus of suicide notes in English, CEASE-v2.0. Experiments show that our proposed multitask framework outperforms the existing state-of-the-art system by 3.78% in the Emotion task, with a cross-validation Mean Recall (MR) of 60.90%. From our empirical and qualitative analysis of results, we observe that learning the tasks of temporality and sentiment together has a clear correlation with emotion recognition.