# Detecting Emotion Drift in Mental Health Text Using Pre-Trained Transformers

arXiv: https://arxiv.org/abs/2512.13363 · HTML: https://mlsanity.com/paper/2512.13363/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 2 of 2)

- [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024)
- [Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning](https://mlsanity.com/paper/2406.02251/) (2024)

## Abstract (arXiv)

This study investigates emotion drift: the change in emotional state across a single text, within mental health-related messages. While sentiment analysis typically classifies an entire message as positive, negative, or neutral, the nuanced shift of emotions over the course of a message is often overlooked. This study detects sentence-level emotions and measures emotion drift scores using pre-trained transformer models such as DistilBERT and RoBERTa. The results provide insights into patterns of emotional escalation or relief in mental health conversations. This methodology can be applied to better understand emotional dynamics in content.