Sentiment Analysis of Online Reviews for Selective Serotonin Reuptake Inhibitors and Serotonin–Norepinephrine Reuptake Inhibitors

Abstract

Background: Depression affects millions worldwide, with drug therapy being the mainstay treatment. A variety of factors, including personal reviews, are involved in the success or failure of medication therapy. This study looked to characterize the sentiment of online medication reviews of Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin–Norepinephrine Reuptake Inhibitor (SNRIs) used to treat depression. Methods: The publicly available data source used was the Drug Review Dataset from the University of California Irvine Machine Learning Repository. The dataset contained the following variables: ID, drug name, condition, review, rating, date, and usefulness count. This study utilized a sentiment analysis of free-text, online reviews via the sentimentr package. A Mann–Whitney U test was used for comparisons. Results: The average sentiment was higher in SSRIs compared to SNRIs (0.065 vs. 0.005, p < 0.001). The average sentiment was also found to be higher in high-rated reviews than in low-rated reviews (0.169 vs. −0.367, p < 0.001). Ratings were similar in the high-rated SSRI group and high-rated SNRI group (9.19 vs. 9.19). Conclusions: This study supports the use of sentiment analysis using the AFINN lexicon, as the lexicon showed a difference in sentiment between high-rated reviews from low-rated reviews. This study also found that SNRIs have more negative sentiment and lower-rated reviews than SSRIs.

A sentiment analysis on online psychiatrist reviews to identify clinical attributes of psychiatrists that shape the therapeutic alliance

“…Hence, patient reviews of these psychiatrists may not represent the full spectrum of patient perceptions of different forms of psychiatric care, either in the United States or abroad. Nevertheless, the 6,400 patient reviews examined in this sample nevertheless are on par with the sample size found in other sentiment analysis studies of patient reviews in mental healthcare (12). Given today's accessibility of different publicly available physician rating websites across the globe, future studies may extend this approach by including other physician rating platforms and identifying common attributes of psychiatrists that positively shape patients' perception of psychiatrists.…”

“…Sentiment analysis is a machine learning-based technique for rating positive or negative use of language in text that recently led to an increased understanding of clinical contributors to patient satisfaction in surgical fields (10,11). In the field of psychiatry, previous studies have demonstrated the validity of sentiment analysis among antidepressant users and for online mental health consultation services (12,13). Specifically, while the general attitude toward online mental health consultation services and the usefulness of online reviews in China have previously been examined, no studies to date have elucidated impactful attributes of psychiatrists that can improve patient experiences during their mental health visits in the United States using PRWs (13).…”

Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter

“…The AFINN tool has been used to analyze the sentiment included in tweets about Asperger [ 5 ], and to compare the sentiment towards different COVID-19 vaccines expressed in social media posts [ 27 ]. The AFINN tool has also been used to analyze free short text, such as answers given in surveys [ 28 , 29 ], web-based reviews [ 30 ], self-reported notes [ 31 ], or descriptions of public health campaigns [ 32 ].…”