Leveraging Empathy, Distress, and Emotion for Accurate Personality Subtyping from Complex Human Textual Responses (2023)

Abstract: Automated personality subtyping is a crucial area of research with diverse applications in psychology, healthcare, and marketing. However, current studies face challenges such as insufficient data, noisy text data, and difficulty in capturing complex personality traits. To address these issues, including empathy, distress, and emotion as auxiliary tasks in automated personality subtyping may enhance accuracy and robustness. This study introduces a Multi-input Multi-task Framework for Personality, Empathy, Distress, and Emotion Detection (MultiPEDE). This framework harnesses the complementary information from empathy, distress, and emotion tasks (auxiliary tasks) to enhance the accuracy and generalizability of automated personality subtyping (the primary task). The model uses a novel deep-learning architecture that captures the interdependencies between these constructs, is end-to-end trainable, and does not rely on ensemble strategies, making it practical for real-world applications. Performance evaluation involves labeled examples of five personality traits, two classes each for personality, empathy, and distress detection, and seven classes for emotion detection. This approach has diverse applications, including mental health diagnosis, improving online services, and aiding job candidate selection.

## introduction language development is integral to personality, allowing individuals to communicate and understand one another. personality is a multifaceted concept, encompassing behaviors, cognition, emotions, and thinking styles. personality subtyping classifies individuals by their traits. the internet's rise yields vast textual data, making computer-driven personality inference a pivotal research domain. personality subtyping finds applications in psychology, healthcare, and marketing, aiding mental health diagnosis, enhancing online user experiences, and aiding candidate selection. automated personality subtyping, a critical research domain, employs machine learning to classify individuals into subtypes. textual data, rich and accessible, fuels this research, with natural language processing (nlp) pivotal in analysis. challenges such as data scarcity and noisy text have spurred researchers to develop efficient methods for accurate personality subtyping. empathy, distress, and emotion are crucial psychological aspects intricately tied to personality traits. empathy, associated with agreeableness, involves understanding and sharing others' feelings. distress, linked to neuroticism, encompasses negative emotions like anxiety. emotion, spanning experiences from happiness to sadness, correlates with traits like extraversion and openness. integrating these facets as auxiliary tasks in automated personality analysis enhances accuracy and provides valuable insights. correlation coefficients were computed among personality traits (conscientiousness, openness, extraversion, agreeableness, and stability) using the wassa 2022 shared task dataset @xcite . the results, presented in the observed correlations corroborate established research on personality traits. for instance, studies consistently demonstrate that conscientiousness positively correlates with stability, agreeableness, and openness to experience, aligning with the dataset's correlation coefficients. similarly, extraversion is known to be positively associated with agreeableness @xcite , reflecting the observed correlation coefficients. moreover, prior research @xcite has identified positive relationships between openness to experience and other traits, including conscientiousness, agreeableness, and stability, which are consistent with the observed correlation coefficients. nevertheless, it's crucial to emphasize that correlation coefficients signify the strength of associations between variables and do not establish causality. further research is needed to ascertain causality. these findings suggest that comprehending one personality trait can yield valuable insights into others, underscoring the potential of jointly learning personality traits to gain a more holistic understanding of an individual's personality. learning personality traits jointly can be advantageous, offering a comprehensive perspective on an individual's personality. this approach has practical implications in psychology, human resources, and marketing, aiding mental health understanding, employee-job fit, and predicting consumer behavior. motivated by these insights, we propose a multitasking framework for personality subtyping, incorporating empathy, distress, and emotion as auxiliary tasks. this framework leverages complementary information to enhance automated personality subtyping accuracy and generalizability. it aims to address limitations in data, noisy text, and feature selection. its applications span improved mental health treatment, enhanced online user experiences, and more effective job candidate selection. the primary contributions are as follows: ## related work recent research has explored the detection of personality traits, emotions, empathy, and distress from textual inputs. however, challenges persist in advancing this field. this section discusses pertinent studies in this domain. ## methodology in this section, we define our task objective and introduce our proposed approach called multi-input multi-task framework for personality, empathy, distress, and emotion detection (multipede). a visual representation of the general architecture of our approach is provided in figure 1 . ## experimental setup we employ two datasets: the wassa shared task 2022 dataset @xcite , encompassing tracks like empathy prediction and emotion classification, and the first impressions v2 dataset (ponce-lópez et al., 2016). several baseline methods are compared to our proposed approach, including convolutional neural network (cnn) @xcite , hierarchical attention network (han) @xcite , cnn+clstm @xcite , bert @xcite , mt-bert @xcite , and cascaded multitask system with external knowledge infusion (cmseki) @xcite . in interest of space, we cover the specifics of the datasets, hyperparameters and evaluation metrics in section a.1 and the above baselines in section a.2 of the appendix. ## results and analysis our experimental results are thoroughly analyzed in comparison to the baseline models. additionally, we conduct ablation studies and extensive qualitative analysis to provide further insight into its performance and strengthen our claims. the results presented in ## conclusion in conclusion, the correlation analysis revealed significant relationships among different personality traits, highlighting the benefits of jointly understanding these traits for a comprehensive view of an individual's personality. our proposed multitasking framework for personality subtyping aims to leverage the complementary information provided by empathy, distress, and emotion as auxiliary tasks to improve the accuracy and generalizability of automated personality subtyping. it differs from existing studies by predicting these constructs from textual inputs using an end-to-end trainable deep-learning architecture, making it practical for real-world applications. this research contribution advances the field by providing a more comprehensive and accurate prediction of these constructs from textual inputs. future research directions include extending the framework to predict mood and sentiment, developing more comprehensive datasets, exploring cultural influences on personality traits, implementing explainable ai techniques, and integrating multimodal data like audio and video for improved accuracy and robustness in personality subtyping models. overall, this study provides a foundation for multitasking personality subtyping and opens avenues for future research. naitian zhou and david jurgens. 2020. condolence and empathy in online communities. in proceedings of the 2020 conference on empirical methods in natural language processing (emnlp), pages 609-626.