LLM-Augmented Therapy Normalization and Aspect-Based Sentiment Analysis for Treatment-Resistant Depression on Reddit

2026-03-12

AI Summary

This study addresses the underrepresentation of real-world patient experiences with pharmacological treatments in clinical trials for treatment-resistant depression (TRD). To bridge this gap, the authors curated a corpus of 23,399 drug mentions from TRD-related posts on Reddit and developed a fine-grained aspect-based sentiment analysis approach. Their method integrates large language models for drug name normalization and data augmentation, followed by fine-tuning of DeBERTa-v3. Evaluated on the SMM4H test set, the model achieves a micro F1-score of 0.800, marking the first large-scale, drug-oriented sentiment quantification in TRD patient-generated content. The analysis reveals that conventional antidepressants are associated with higher proportions of negative sentiment, whereas ketamine-class drugs elicit more positive evaluations.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Treatment-resistant depression (TRD) is a severe form of major depressive disorder in which patients do not achieve remission despite multiple adequate treatment trials. Evidence across pharmacologic options for TRD remains limited, and trials often do not fully capture patient-reported tolerability. Large-scale online peer-support narratives therefore offer a complementary lens on how patients describe and evaluate medications in real-world use. In this study, we curated a corpus of 5,059 Reddit posts explicitly referencing TRD from 3,480 subscribers across 28 mental health-related subreddits from 2010 to 2025. Of these, 3,839 posts mentioned at least one medication, yielding 23,399 mentions of 81 generic-name medications after lexicon-based normalization of brand names, misspellings, and colloquialisms. We developed an aspect-based sentiment classifier by fine-tuning DeBERTa-v3 on the SMM4H 2023 therapy-sentiment Twitter corpus with large language model based data augmentation, achieving a micro-F1 score of 0.800 on the shared-task test set. Applying this classifier to Reddit, we quantified sentiment toward individual medications across three categories: positive, neutral, and negative, and tracked patterns by drug, subscriber, subreddit, and year. Overall, 72.1% of medication mentions were neutral, 14.8% negative, and 13.1% positive. Conventional antidepressants, especially SSRIs and SNRIs, showed consistently higher negative than positive proportions, whereas ketamine and esketamine showed comparatively more favorable sentiment profiles. These findings show that normalized medication extraction combined with aspect-based sentiment analysis can help characterize patient-perceived treatment experiences in TRD-related Reddit discourse, complementing clinical evidence with large-scale patient-generated perspectives.

Problem

• treatment-resistant depression
• patient-reported outcomes
• medication sentiment
• real-world evidence
• aspect-based sentiment analysis

Innovation

• LLM-augmented data augmentation
• aspect-based sentiment analysis
• therapy normalization
• treatment-resistant depression
• DeBERTa-v3 fine-tuning