Previously submitted to: JMIR Dermatology (no longer under consideration since Oct 21, 2024)
Date Submitted: Jul 20, 2023
Open Peer Review Period: Jul 20, 2023 - Sep 14, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Sentiment analysis and natural language processing using Reddit data to evaluate patient opinions on hair loss therapeutics
ABSTRACT
Background:
Online forums are rich sources of user-derived data and harnessing this information utilizing natural language processing techniques can provide insights into patient experiences. The subject forums of Reddit may allow for focused explorations such as opinions on specific therapeutic agents.
Objective:
To determine patient sentiment about key treatments for female hair loss. Secondarily, to demonstrate the feasibility of using Reddit data to perform sentiment analysis on patient comments.
Methods:
A software pipeline scraped publicly available Reddit comments from r/femalehairloss, then processed them into sentence tokens. Sentiment analysis was subsequently performed. A frequency word representation was created.
Results:
The most frequently cited single treatments were minoxidil and spironolactone. Comments mentioning PRP and minoxidil were the second and third most positive on average. Comments referencing dutasteride were the most positive, however, this may be skewed by the low number of dutasteride-only comments. Finasteride comments were the least positive on average but were still slightly greater than 0.
Conclusions:
In this paper, we have demonstrated the feasibility of performing sentiment analysis on Reddit comments. Our results suggest that opinions about hair loss therapeutics on the examined forum were on average positive. Analysis of health-focused subreddits such as r/femalehairloss can provide a deeper understanding of patient discourse and may also represent an opportunity for physicians to disseminate evidence-based recommendations.
Citation
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Copyright
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