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

TL;DR

This paper presents a method using pre-trained transformer models to detect and measure emotion drift within mental health texts, providing insights into emotional changes during conversations.

Contribution

It introduces a novel approach for sentence-level emotion detection and drift measurement in mental health messages using transformers.

Findings

Transformer models effectively detect emotion shifts.

Emotion drift patterns reveal escalation or relief in mental health texts.

Method enhances understanding of emotional dynamics in mental health communication.

Abstract

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.