Sentiment Analysis of Autologous Breast Reconstruction Using Natural Language Processing and Deep Learning.

ABSTRACT:

Background

Use of autologous flaps for postmastectomy reconstruction has increased. Public forums offer large-scale patient narratives that can be analyzed with natural language processing (NLP) models, such as RoBERTa.

Objectives

This study aims to assess patient-reported sentiment and emotions toward autologous reconstruction with deep inferior epigastric perforator (DIEP), transverse rectus abdominis myocutameous (TRAM), and latissimus dorsi (LD) flaps.

Methods

Public reviews from RealSelf (Seattle, WA) referencing DIEP, TRAM, or LD flaps were collected. Two NLP models were applied: (1) a binary sentiment classifier (positive/negative) and (2) an emotion model scoring fear, sadness, anger, disgust, neutral, surprise, and joy; each review was labeled by its highest-

SUBMITTER: Wang MJ 

PROVIDER: S-EPMC12728504 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature