Predicting Depressive Symptoms through Emotion Pairs within Asian American Families

2026-02-03

AI Summary

This study investigates how ambivalent emotions arising from parent–child relationships in Asian American families predict depressive symptoms. Leveraging narrative texts from Asian and Asian American offspring posted on Reddit’s r/Asianparentstories, the research pioneers the integration of emotion pairs and mixed emotions into a depression prediction framework. By combining BERT-based sentence-level emotion recognition with post-level depression detection, the analysis examines patterns of emotional co-occurrence. Findings reveal that negative emotion pairs (e.g., confusion–grief) significantly and positively predict depressive symptoms, whereas positive pairs (e.g., admiration–realization) exhibit a negative association. Moreover, mixed-emotion combinations demonstrate complex and varied predictive effects, underscoring the critical role of emotional interplay in mental health outcomes.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Emotional Intelligence
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• depressive symptoms
• emotion pairs
• Asian American families
• emotional ambivalence
• parent-child relationships

Innovation

• BERT-based emotion detection
• emotion pairs
• emotional ambivalence
• depressive symptom prediction
• online narrative analysis