Predicting Depressive Symptoms through Emotion Pairs within Asian American Families

TL;DR

This study uses a BERT-based model to analyze emotion pairs in online narratives of Asian American children, revealing how emotional ambivalence correlates with depressive symptoms and emphasizing the importance of automated emotion detection.

Contribution

It introduces a novel approach to detect and analyze mixed emotions in online narratives, highlighting the predictive value of emotion pairs for depressive symptoms in Asian American families.

Findings

Negative emotion pairs are associated with depressive symptoms.

Positive emotion pairs negatively correlate with depressive symptoms.

Emotional ambivalence shows varied effects on depression prediction.

Abstract

Studies on intergenerational relationships between parents and children in Asian American families highlight their impact on mental health and well-being. This study investigates the role of ambivalent emotions in online narratives shared by Asian and Asian American children on the subreddit, r/Asianparentstories. By employing a BERT-based model to detect emotion at the sentence level and depressive symptoms at the post level, we analyze mixed feelings to better understand how they predict depressive symptoms. First, among 28 detectable, eight (realization, approval, sadness, anger, curiosity, annoyance, disappointment, disapproval) comprise over 50%, exhibiting significant co-occurrence among themselves and with other emotions. Second, we find the co-occurrence of multiple emotions, indicating that emotions in a single post are not limited to consistently positive or negative feelings. Finally, our findings indicate that while negative emotion pairs (e.g., confusion-grief, anger-grief) are associated with depressive symptoms, positive emotion pairs (e.g., admiration-realization, amusement-joy) negatively correlate with depressive symptoms, and combinations of ambivalent emotions indicate varied results in predicting depressive symptoms. These findings highlight the importance of automated emotion classification and the need to consider emotional ambivalence, which holds practical and clinical implications for understanding the dynamics of parent-child relationships.