EM - PERSONA : EM otion-assisted Deep Neural Framework for PERSONA lity Subtyping from Suicide Notes (2022)

Abstract: The World Health Organization has emphasised the need of stepping up suicide prevention efforts to meet the United Nation’s Sustainable 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 personality 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-performing single-task systems.

## introduction suicide continues to be one of the significant causes of death worldwide @xcite . given the significance of personality as a basis for understanding psychopathology @xcite and the variability in risk factors associated with suicide, subtyping patients based on their personalities can provide greater specificity than simple comparisons of suicidal to non-suicidal individuals @xcite . @xcite showed that emotions such as anger, aggressiveness, anxiety, and sadness were associated with personality traits of individuals who attempted suicide. we quote a few excerpts from suicide notes (sns) and online personality posts (pps) in our primary contributions are two-fold: we present a novel corpus of suicide notes annotated with personality traits across four dichotomies and develop an end-to-end multi-task emotion-assisted system for simultaneous detection of these traits from suicide notes. ## related work the myers-briggs type indicator (mbti) @xcite , based on psychiatrist carl jung's ideas, is a popular personality metric that employs four dichotomies as indications of personality traits: introversion (i) / extraversion (e), intuition (n) / sensing (s), thinking (t) / feeling (f), judging (j) / perceiving (p). another popular model like mbti is the big five (goldberg, 1993) that produces very specific and individual results, which can be tedious to draw general insights and advice from test results making the practical application of the knowledge very difficult, especially when the data is scarce (as in the case of suicide note corpus). the fact that the categories for the big five personality traits are too wide and absolute to offer any meaningful insight is another issue with them. humans are adaptive beings that adapt to their environment. in situations where we are around close friends, for instance, we could be more open, whilst in foreign settings you might be less open. artificial intelligence (ai) is already playing a crucial role in mental healthcare (chatbot: woebot, virtual assistant: ellie (d'alfonso, 2020)) in handling the increased demand for services, stretched workloads, high costs for treatment, and associated stigma with mental illness @xcite . more recently, personality detection studies @xcite @xcite ) using computational methods have gained traction especially transformer-based @xcite pretrained language models. however, the existing suicide note corpora @xcite are annotated at the sentence level and existing studies do not exploit the emotional content inherent in them. this motivated us to devise an approach for utilizing the sentence-level information inherent in the existing datasets and address the closely associated tasks at the document level. moreover, statistical analysis @xcite and feature engineering @xcite have been used in the bulk of the studies on this topic. however, most of the research on personality subtyping has been on domains like essays (big five dataset (pennebaker and king, 1999)) and social media (mbti dataset) and none on the domain of suicide. this is the first attempt, to our knowledge, to identify personality subtypes of individuals who have completed suicide. ## dataset we consider the benchmark cease-v2.0 dataset foot_1 @xcite which is a fine-grained emotion annotated suicide notes corpus containing 4,932 sentences from suicide notes, each annotated independently (without any contextual information) with multi-label emotions from 15 fine-grained emotion classes. ## methodology here, we discuss the proposed emotion-assisted deep neural framework for personality subtyping (em-persona). the overall architecture is illustrated in fig. 1 . sn s 1 bert encoder conv + max pool conv + max pool attention attention . . . ## experiments and results in this section, we discuss the experiments performed and the results and analysis. ## results and discussion ablation study: to test the impact of the emotion-assisting setup, we remove the emotionspecific dense layers in em-persona and see a notable drop in scores across all the personality subtasks (shown in ## conclusion our study focuses on artificial intelligence's assistive role, emphasising that cognitive technology is designed to enhance human intelligence rather than replace it. the proposed method is developed to serve practitioners (computer-aided diagnosis and learning) and individuals (self-monitoring) in their combined effort toward low-profile first-hand evaluation of their personalities. the findings of this study imply that (1) present state-of-the-art methods, both conversational and document encoding methods in general, fail to comprehend personality information in suicide notes to a substantial extent, (2) to improve overall system performance at the document level (such as depression, perceived burdensomeness, and thwarted belongingness), sentence-level information (such as temporal orientation, sentiment, and emotion) can be incorporated into document representations produced by existing transformer architectures, and, (3). large personality traits annotated balanced corpora are required to obtain solid findings, and the introduced resource can facilitate related studies. identifying key subgroups of people with suicidal inclinations will help us better understand risk factors and therapies based on subtypes. in future work, we want to address the two major limitations of our study. first, personality traits are not so simple that they can be squeezed into fixed binary categories across four dimensions, as examined in this study. second, the short context length problem may be addressed by testing with much bigger datasets than the one used in this work.