EM-PERSONA: EMotion-assisted Deep Neural Framework for PERSONAlity Subtyping from Suicide Notes

Asif Ekbal, Soumitra Ghosh, Dhirendra Kumar Maurya, Pushpak Bhattacharyya

Abstract:

The World Health Organization has emphasised the need of stepping up suicide prevention efforts to meet the United Nation’s Sustain-
able Development Goal target of 2030 (Goal 3:Good health and well-being). We address the challenging task of personality subtyping from suicide notes. Most research on personality subtyping has relied on statistical analysis and feature engineering. Moreover, state-of-the-art transformer models in the automated personal ity subtyping problem have received relatively less attention. We develop a novel EMotion-assisted PERSONAlity Detection Framework (EM-PERSONA). We annotate the benchmark CEASE-v2.0 suicide notes dataset with personality traits across four dichotomies: Introversion (I)-Extraversion (E), Intuition (N)-Sensing (S), Thinking (T)-Feeling (F), Judging (J)–Perceiving (P). Our proposed method outperforms all baselines on comprehensive evaluation using multiple state-of-the-art systems.
Across the four dichotomies, EM-PERSONA improved accuracy by 2.04%, 3.69%, 4.52%, and 3.42%, respectively, over the highest per-forming single-task systems.