Sentiment trajectory modeling in mental health discussions via transformer-based analysis

Area of Science:

• Computational linguistics
• Mental health informatics
• Social media analytics

Background:

• Social media text offers insights into mental health.
• Current sentiment analysis methods treat social media posts as static, ignoring emotional dynamics.

Purpose of the Study:

• To develop a novel framework for Temporal Sentiment Progression Analysis.
• To capture the evolution of emotional states, including variability and inflection points, in social media users.
• To correlate identified sentiment progression archetypes with mental health concerns.

Main Methods:

• Utilized domain-specific transformer architectures for sentiment and thematic classification.
• Employed post hoc statistical analysis to examine temporal patterns in user discussions.
• Evaluated the framework on approximately 4,000 Reddit comments from mental health subreddits.

Main Results:

• Identified distinct sentiment progression archetypes strongly correlated with self-reported mental health issues.
• Achieved a classification accuracy of 92% for the proposed framework.
• Demonstrated the framework's ability to capture emotional evolution dynamics.

Conclusions:

• The shift from static to dynamic sentiment analysis provides a more nuanced understanding of emotional distress.
• This approach can aid in developing context-aware interventions for mental health.
• Temporal Sentiment Progression Analysis offers a promising tool for mental health monitoring via social media.