LLM-Augmented Therapy Normalization and Aspect-Based Sentiment Analysis for Treatment-Resistant Depression on Reddit
Yuxin Zhu, Sahithi Lakamana, Masoud Rouhizadeh, Selen Bozkurt, Rachel Hershenberg, Abeed Sarker
arXiv Preprint Archive March 12, 2026 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study |
|---|---|
| Sample size | 3,480 |
| Population | Reddit subscribers posting about treatment-resistant depression in mental health-related subreddits |
| Topics | Depression |
| Keywords | Cs.cl |
| Key findings | Conventional antidepressants show consistently higher negative than positive sentiment proportions, while ketamine and esketamine show comparatively more favorable sentiment profiles in Reddit discourse. |
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.
Comparable studies
Other observational and cohort studies on depression, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Metacognitive awareness and prevention of relapse in depression: Empirical evidence. Residually depressed patients and nondepressed controls; recovered depressed patients | 2002 | Observational cohort with intervention components | |
| Psilocybin for treatment-resistant depression: fMRI-measured brain mechanisms. Patients with treatment-resistant depression | 2017 | Observational cohort | n = 19 |
| Antidepressant effects of a single dose of ayahuasca in patients with recurrent depression: a preliminary report Patients with a depressive disorder | 2015 | Observational study | |
| Concomitant BDNF and sleep slow wave changes indicate ketamine-induced plasticity in major depressive disorder Patients with treatment-resistant major depressive disorder | 2012 | Observational cohort | n = 30 |
| Altered peripheral immune profiles in treatment-resistant depression: response to ketamine and prediction of treatment outcome Healthy controls and actively depressed patients with treatment-resistant depression... | 2017 | Observational cohort | n = 59 |