Sentiment and concern evaluation using online health community reviews

Abstract

In the online health community (OHC), each patient review of doctors includes an evaluation and an emotional attitude toward the doctor. Subsequent patients usually browse the comments of other patients about doctors when choosing a doctor and subsequently make decisions based on these reviews. Through sentiment analysis, a user's emotional orientation can be judged from the review, enabling an understanding of patients' emotional tendencies and main concerns regarding doctors during medical treatment. This also provides a reference for OHC doctors to improve service quality. This study used a method based on a sentiment dictionary to analyze the sentiment value of reviews and selected three different types of diseases (diabetes, leukemia, and depression) as examples from user reviews of the “Good Doctor Online” community. SnowNLP, a Python library for Chinese natural language processing, was used to realize the sentiment analysis of the reviews. The program correctly identified the sentiment of most reviews. Although the sentiments of OHC reviews are mostly positive, there are also a few extremely negative reviews. Most positive patient reviews about doctors are related to their good attitude and patience with patients and their condition.

Keywords

Online health community / Patient review data / Sentiments / Sentiment analysis / Sentiment dictionary-based

Funding

None.

Conflict of interest

The authors declare that they have no competing interests.

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