CALLM: Context-Aware Emotion Analysis in Cancer Survivors Using LLMs and Retrieval-Augmented Mobile Diaries

• URL: http://arxiv.org/abs/2503.10707v1
• Date: Wed, 12 Mar 2025 18:36:41 GMT
• Title: CALLM: Context-Aware Emotion Analysis in Cancer Survivors Using LLMs and Retrieval-Augmented Mobile Diaries
• Authors: Zhiyuan Wang, Katharine E. Daniel, Laura E. Barnes, Philip I. Chow,
• Abstract summary: CALLM is a context-aware emotion analysis framework that analyzes mobile diary entries from cancer survivors to predict their emotional states.<n>We collected a large-scale dataset of cancer survivors' mobile ecological momentary assessments (EMAs)<n>Results demonstrate strong performance of CALLM, with balanced accuracies reaching 72.96% for positive and 73.29% for negative affect, and 73.72% for predicting individual's desire to regulate emotions.
• Score: 11.00553400353042
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cancer survivors face unique emotional challenges that impact their quality of life. Mobile diary entries-short text entries recording through their phone about their emotional experiences-provide a promising method for tracking these experiences in real time. Although emotion analysis tools show potential for recognizing emotions from text, current methods lack the contextual understanding necessary to accurately interpret the brief, personal narratives in mobile diaries. We propose CALLM, a context-aware emotion analysis framework that leverages Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), to analyze mobile diary entries from cancer survivors to predict their emotional states. The framework enhances prediction accuracy beyond existing methods by (1) integrating retrieved peer experiences as contextual examples and (2) incorporating individuals' temporal emotional trajectories from their mobile diary entries. We collected a large-scale dataset (N=407) of cancer survivors' mobile ecological momentary assessments (EMAs), which assessed positive and negative affect, desire to regulate emotions, social interaction quality, and availability for interventions, alongside daily mobile diary entries in an open response format regarding what was driving their current emotional experience. Results demonstrate strong performance of CALLM, with balanced accuracies reaching 72.96% for positive and 73.29% for negative affect, and 73.72% for predicting individual's desire to regulate emotions. Post-hoc analysis reveals that leveraging model confidence, encouraging longer diary entries, and incorporating personal ground truth, further enhance predictive outcomes. Our findings support the feasibility of deploying LLM-powered emotion analysis in chronic health populations and suggest promising directions for personalized interventions for cancer survivors.

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