Background Clinical data in social media are an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes, and preferences. Objective This tutorial aims to describe a novel, robust, and modular method for the sentiment analysis and emotion detection of free text from web-based forums and the factors to consider during its application. Methods We mined the discussion and user information of all posts containing search terms related to a medical subspecialty (oculoplastics) from MedHelp, the largest web-based platform for patient health forums. We used data cleaning and processing tools to define the relevant subset of results and prepare them for sentiment analysis. We executed sentiment and emotion analyses by using IBM Watson Natural Language Understanding to generate sentiment and emotion scores for the posts and their associated keywords. The keywords were aggregated using natural language processing tools. Results Overall, 39 oculoplastic-related search terms resulted in 46,381 eligible posts within 14,329 threads. Posts were written by 18,319 users (117 doctors; 18,202 patients) and included 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify the most prominent topics, including specific symptoms, medication, and complications. The sentiment and emotion scores of these keywords and eligible posts were analyzed to provide concrete examples of the potential of this methodology to allow for a better understanding of patients’ attitudes. The overall sentiment score reflects a positive, neutral, or negative sentiment, whereas the emotion scores (anger, disgust, fear, joy, and sadness) represent the likelihood of the presence of the emotion. In keyword grouping analyses, medical signs, symptoms, and diseases had the lowest overall sentiment scores (−0.598). Complications were highly associated with sadness (0.485). Forum posts mentioning body parts were related to sadness (0.416) and fear (0.321). Administration was the category with the highest anger score (0.146). The top 6 forum subgroups had an overall negative sentiment score; the most negative one was the Neurology forum, with a score of −0.438. The Undiagnosed Symptoms forum had the highest sadness score (0.448). The least likely fearful posts were those from the Eye Care forum, with a score of 0.260. The overall sentiment score was much more negative before the doctor replied. The anger, disgust, fear, and sadness emotion scores decreased in likelihood, whereas joy was slightly more likely to be expressed after doctors replied. Conclusions This report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during its application include evaluating the scope of the search; selecting search terms and understanding their linguistic usages; and establishing selection, filtering, and processing criteria for posts and keywords tailored to the desired results.

Background: Upper and lower blepharoplasty are among the most common procedures in aesthetic surgery and are often emotionally laden due to the subjective nature of outcomes and implications with beauty and self-identity. This article capitalizes on the increasing wealth of patient-provided health information online and is the first to analyze the emotions surrounding blepharoplasty discussions in an open internet health forum, MedHelp. Methods: We used Python to scrape MedHelp for threads that contained “blepharoplasty” and then used IBM Watson Natural Language Understanding to perform sentiment analyses, calculating a general sentiment score (−1 to +1) as well as emotion scores for anger, sadness, joy, fear, and disgust (0 to 1) for posts and keywords contained within the posts. Keywords were then manually grouped into five distinct clinical categories: symptoms, doctor, treatment, medication, and body. Results: We collected 52 threads containing “blepharoplasty,” yielding 154 posts and 1365 keywords. The average sentiment score was negative among all posts (−0.15) and keywords (−0.30). Among all posts and keywords, sadness had the highest score and disgust had the lowest score. Conclusions: Fear and sadness are the predominant emotions for blepharoplasty patients online, and the most negative symptoms cited are not ones that surgeons typically expect.

BACKGROUND Clinical data present in social media is an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes and preferences. OBJECTIVE We describe a novel and broadly applicable method for sentiment analysis and emotion detection to free text from online medical health forums and the factors to consider during its application. METHODS We mined the full discussion and user information of all posts containing search terms related to a specific medical subspecialty (oculoplastics) from MedHelp, the largest online platform for patient health forums. We employed a variety of data cleaning and processing to define the relevant subset of results and prepare those results for sentiment analysis. We executed sentiment and emotion analysis through IBM Watson Natural Language Understanding service to generate sentiment and emotion scores for the posts and their associated keywords. Keywords were aggregated using natural language processing tools. RESULTS 39 oculoplastics-related search terms resulted in 46,381 eligible posts within 14,329 threads, written by 18,319 users (117 doctors; 18,202 patients) and 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify most prominent topics, including specific symptoms, medication and complications. The sentiment and emotion scores of these keywords and eligible posts were further analyzed to provide concrete examples of the methodology’s potential to allow better understanding of patients’ attitudes. CONCLUSIONS This comprehensive report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during application include evaluating the scope of the search, selecting search terms and understanding their different linguistic usages, and establishing robust selection, filtering and processing criteria for posts and keywords tailored to the results.