Research on the Characteristics and Usefulness of User Reviews of Online Mental Health Consultation Services: A Content Analysis

Liu, Jingfang; Gao, Lu

doi:10.3390/healthcare9091111

Cited by 4 publications

(4 citation statements)

References 60 publications

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“…The lexicon is a list of words, each associated with corresponding emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and sentiments (positive and negative); this classification is in line with the work of Plutchik and his wheel of emotions [ 22 ]. The NRC lexicon has been increasingly applied in recent years for quantifying affect in online textual data [ 23 – 25 ].…”

Section: Methodsmentioning

confidence: 99%

The emotional side of taking part in a cancer clinical trial

et al. 2023

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Background Taking part in a cancer clinical trial often represents a source of psychological distress and emotional activation among patients and their caregivers. Nowadays, social media platforms provide a space for these groups to freely express and share their emotional experiences. Aims We aimed to reveal the most prevalent basic and complex emotions and sentiments in the posts of the patients and caregivers contemplating clinical trials on Reddit. Additionally, we aimed to categorize the types of users and posts. Methods With the use of keywords referring to clinical trials, we searched for public posts on the subreddit ‘cancer’. R studio v. 4.1.2 (2021-11-01) and NRC Emotion Lexicon was used for analysis. Following the theoretical framework of Plutchik’s wheel of emotions, the analysis included: 8 basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and 4 types of complex emotions (primary, secondary, tertiary, and opposite dyads). We utilized the package ‘PyPlutchik’ to visualize the emotion wheels in Python 3.10.5. Results A total of 241 posts were included in the final database. User types (129 patients, 112 caregivers) and post types (142 expressed shared experience, 77 expressed advice, and 85 conveyed both) were identified. Both positive (N = 2557, M = .68) and negative (N = 2154, M = .57) sentiments were high. The most prevalent basic emotions were: fear (N = 1702, M = .45), sadness (N = 1494, M = .40), trust (N = 1470, M = .44), and anticipation (N = 1376, M = .37). The prevalence of complex/dyadic emotions and their interpretation is further discussed. Conclusion In this contribution, we identified and discussed prevalent emotions such as fear, sadness, optimism, hope, despair, and outrage that mirror the psychological state of users and affect the medical choices they make. The insights gained in our study contribute to the understanding of the barriers and reinforcers to participation in trials and can improve the ability of healthcare professionals to assist patients when confronted with this choice.

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Section: Methodsmentioning

confidence: 99%

The emotional side of taking part in a cancer clinical trial

et al. 2023

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“…Brzustewicz P et al [16] used LDA for topic modeling and the Louvain algorithm for semantic network clustering to analyze sustainable consumption during the COVID-19 pandemic. Liu J et al [17] used LDA topic modeling, dictionary-based sentiment analysis and the NRC Word-Emotion Association Lexicon to extract the topics, sentiments and context features of user reviews of online mental health consultation services. Zhang N et al [18] crawled online reviews of express companies and used an LDA model and sentiment analysis to identify service attributes and customer satisfaction so as to analyze user-generated content and provide a scientific service innovation scheme for express enterprises.…”

Section: Text Feature Extractionmentioning

confidence: 99%

A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Zhou

Wei

et al. 2021

Aerospace

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Traditional aircraft maintenance support work is mainly based on structured data. Unstructured data, such as text data, have not been fully used, which means there is a waste of resources. These unstructured data contain a great storehouse of fault knowledge, which could provide decision support for aircraft maintenance support work. Therefore, a text-based fault diagnosis model is proposed in this paper. The proposed method uses Word2vec to map text words into vector space, and the extracted text feature vectors are then input into the classifier based on a stacking ensemble learning scheme. Its performance has been validated using a real aircraft fault text dataset. The results show that the fault diagnosis accuracy of the proposed method is 97.35%, which is about 2% higher than that of the suboptimal method.

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“…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).…”

Section: Introductionmentioning

confidence: 99%

“…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). To that end, the present study aimed to utilize sentiment analysis on online written reviews of psychiatrists from PRWs to identify demographic and clinical characteristics strongly associated with reviews with the most positive and negative sentiment.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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BackgroundWhile online reviews from physician rating websites are increasingly utilized by healthcare providers to better understand patient needs, it remains difficult to objectively identify areas for improvement in providing psychiatric care.ObjectivesTo quantitatively characterize the sentiment of online written reviews of psychiatrists to determine clinical attributes that can be strengthened to improve psychiatrists’ therapeutic alliance with their patients.Materials and methodsSentiment scores of 6,400 written reviews of 400 US-based psychiatrists on a US-based online physician rating website were obtained through a natural-language-processing-based sentiment analysis. Relationships among sentiment scores, average star ratings, and demographics were examined. Linguistic analyses determined words and bigrams that were highly associated with reviews with the most positive and negative sentiment.FindingsSentiment scores were significantly correlated with average star ratings of the psychiatrists (R = 0.737, p < 0.001). Psychiatrists who were younger (< 56 years old) and/or practiced in the Northeast had significantly higher average star ratings than those older and/or practicing in the Southwest. Frequency analysis showed that positive reviews most frequently contained “time” (N = 1,138) and “caring” (N = 784) while negative reviews most frequently contained “medication” (N = 495) and “time” (N = 379). Logistic regression analysis revealed that reviews were more likely to be considered positive when they included “great listener” (OR = 16.89) and “comfortable” (OR = 10.72) and more likely to be negative when they included “meds” (OR = 0.55) and “side effect” (OR = 0.59).ConclusionPsychiatrists who are younger and located in the Northeast receive more positive reviews; there may be potential for demographic bias among patient reviewers. Patients positively rate psychiatrists who make them feel heard and comfortable but negatively rate encounters centered around medications and their side effects. Our study lends quantitative evidence to support the importance of thorough and empathetic communication of psychiatrists in building a strong therapeutic alliance.

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Research on the Characteristics and Usefulness of User Reviews of Online Mental Health Consultation Services: A Content Analysis

Cited by 4 publications

References 60 publications

The emotional side of taking part in a cancer clinical trial

The emotional side of taking part in a cancer clinical trial

A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

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

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