[Paper Review] Predicting Depressive Symptoms through Emotion Pairs within Asian American Families

Sangpil Youm, Nari Yoo+1 more|arXiv (Cornell University)|Feb 3, 2026

Mental Health via Writing0 citations

TL;DR

The paper extracts emotions from Reddit posts about Asian parents, builds emotion networks, and uses logistic regression to link emotion pairs to depressive symptoms.

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.

Motivation & Objective

• Motivate the need to understand intergenerational ambivalence in Asian American families and its link to mental health.
• Use Reddit narratives to capture authentic emotional contexts beyond surveys and interviews.
• Identify emotion pairs that significantly predict depressive symptoms.
• Characterize dominant emotions and their interaction patterns within posts to inform culturally informed interventions.

Proposed method

• Detect emotions at the sentence level with EmoRoBERTa.
• Construct per-post emotion networks where nodes are emotions and edge thickness reflects co-occurrence frequency.
• Identify depressive symptoms in posts using DepRoBERTa.
• Treat emotion pairs as predictor variables and depressive symptoms as the outcome in a logistic regression model.
• Perform content analysis to interpret significant emotion pairs.
• Report significant emotion pairs and their odds ratios (e.g., amusement-grief with OR=2.62; sadness-optimism with OR=0.79).

Figure 1: Emotion relationship network. Note. Nodes represent emotions. Link thickness corresponds to size of degree, measured by frequency of co-occurrence

Experimental results

Research questions

• RQ1 Which emotion pairs co-occur in posts about Asian parents, and how do these pairs relate to depressive symptoms?
• RQ2 Which specific emotion pairs significantly predict depressive symptoms in the dataset?
• RQ3 What are the dominant emotions and co-occurrence patterns in Reddit posts on Asian parent–child experiences?
• RQ4 Do mixed/ambivalent emotions more strongly relate to depressive symptoms than single emotions?
• RQ5 How can findings inform culturally informed family interventions for Asian American families?

Key findings

• Eight emotions (realization, approval, sadness, anger, disapproval, annoyance, curiosity, disappointment) account for about 50% of detected emotions.
• Posts often contain more than two emotions, with pairs and triplets being common.
• Amusement-grief is the most impactful emotion pair positively associated with depressive symptoms (OR=2.62).
• Caring-curiosity is the emotion pair with the strongest negative association with depressive symptoms.
• Sadness paired with optimism shows a negative association with depressive symptoms (OR=0.79).
• Ten emotion pairs significantly contribute to depressive symptoms at p<0.05.

Figure 2: Cumulative Distribution Function (CDF) and Complementary Cumulative Distribution Function (CCDF).