LLM-Augmented Therapy Normalization and Aspect-Based Sentiment Analysis for Treatment-Resistant Depression on Reddit
arXiv:2603.12343v1 Announce Type: new 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 la
arXiv:2603.12343v1 Announce Type: new 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.
Executive Summary
This study leverages large-scale online peer-support narratives on Reddit to gain insight into patient-perceived treatment experiences for treatment-resistant depression (TRD). The researchers curated a corpus of 5,059 Reddit posts, developed an aspect-based sentiment classifier, and applied it to quantify sentiment toward individual medications. The findings reveal that conventional antidepressants tend to have negative sentiment profiles, whereas ketamine and esketamine show more favorable sentiment. This study demonstrates the potential of LLM-augmented therapy normalization and aspect-based sentiment analysis in characterizing patient-perceived treatment experiences, complementing clinical evidence with large-scale patient-generated perspectives. The results have significant implications for the development of more effective treatment strategies for TRD.
Key Points
- ▸ Leveraged large-scale online peer-support narratives on Reddit to study treatment-resistant depression (TRD)
- ▸ Developed an aspect-based sentiment classifier using DeBERTa-v3 and fine-tuned it on the SMM4H 2023 therapy-sentiment Twitter corpus
- ▸ Applied the classifier to quantify sentiment toward individual medications on Reddit, revealing patterns by drug, subscriber, subreddit, and year
Merits
Comprehensive dataset
The researchers curated a large corpus of 5,059 Reddit posts, providing a rich source of data for analysis
Novel application of LLM-augmented therapy normalization
The study showcases the potential of LLM-augmented therapy normalization in characterizing patient-perceived treatment experiences
Aspect-based sentiment analysis
The researchers developed and applied an aspect-based sentiment classifier, enabling a nuanced understanding of patient sentiment toward individual medications
Demerits
Limited generalizability
The study relies on a specific dataset of Reddit posts, which may not be representative of the broader population of individuals with TRD
Dependence on LLM performance
The accuracy of the sentiment classifier depends on the performance of the underlying LLM, which may be subject to errors or biases
Expert Commentary
This study marks a significant contribution to the field of digital mental health research and patient-centered care. The use of LLM-augmented therapy normalization and aspect-based sentiment analysis demonstrates the potential of innovative methods in characterizing patient-perceived treatment experiences. However, the study's limitations, such as limited generalizability and dependence on LLM performance, should be addressed in future research. The findings have significant implications for the development of more effective treatment strategies for TRD, and the study's methods may be applied to other areas of mental health research.
Recommendations
- ✓ Future studies should aim to replicate the findings using diverse datasets and populations
- ✓ Clinicians and policymakers should consider incorporating patient-perceived treatment experiences into treatment plans and policy decisions