  The World Health Organization (WHO) has emphasized the importance of
significantly accelerating suicide prevention efforts to fulfill the United
Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper,
we present an end-to-end multitask system to address a novel task of detection
of two interpersonal risk factors of suicide, Perceived Burdensomeness (PB) and
Thwarted Belongingness (TB) from suicide notes. We also introduce a manually
translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the
benchmark CEASE-v2.0 dataset, annotated with temporal orientation, PB and TB
labels. We exploit the temporal orientation and emotion information in the
suicide notes to boost overall performance. For comprehensive evaluation of our
proposed method, we compare it to several state-of-the-art approaches on the
existing CEASE-v2.0 dataset and the newly announced CoMCEASE-v2.0 dataset.
Empirical evaluation suggests that temporal and emotional information can
substantially improve the detection of PB and TB.
