Background: In recent years, online pharmacies have been accepted by increasingly more consumers, and the prospects for online pharmacies are optimistic. This article explores the consumers’ satisfaction factors addressed in Business to Customer (B2C) online pharmacy reviews and analyzes the sentiments expressed in the reviews. The goal of this work is to help B2C online pharmacy enterprises identify consumers’ concerns, continuously improve the health services level. Methods: This article was based on the Latent Dirichlet Allocation (LDA) topic model. From a third-party platform-based B2C online pharmacy and a proprietary B2C online pharmacy (JD Pharmacy and J1.COM, respectively), 136,630 pieces of over-the-counter (OTC) drug review data posted from January 1, 2015 to December 31, 2018 were selected as samples and used to explore the satisfaction factors of B2C online pharmacy consumers regarding the entire drug purchasing process. Then, the sentiments expressed in the drug reviews were analyzed with SnowNLP. Result: Categorization of the 12 factors identified by LDA showed that 5 factors were related to logistics; these 5 factors, which also included the most drug reviews, made up 38.5% of the reviews. The number of factors related to drug prices was second, with 3 factors, and reviews of drug prices made up 25.5% of the reviews. Customer service and drug effects each had two related factors, and a smaller percentage of these reviews (13.95%) were related to drug effects. Consumers still maintain positive opinions of JD Pharmacy and J1.COM. However, some opinions on logistics and drug prices are expressed. Conclusion: The most important task for online pharmacies is to improve logistics. It is better to develop self-built logistics. Both types of B2C online pharmacies can improve consumer viscosity by implementing marketing strategies. With regard to customer service, focusing on improving employees’ service attitudes is necessary.

BACKGROUND Online Health Community (OHC) refers to a forum where patients, their family members, doctors and caregivers communicate with each other. Patients who participate in OHCs can obtain benefits for disease treatments and health management, so identifying the categories of patient needs and how they are satisfied are significant to determining theories of patient demand and community construction. OBJECTIVE (1) Explore the needs of patients in the Internet environment. (2) Distinguish the similarities and differences of patient needs among OHCs of different types and concerning different diseases. (3) Proposed a method for automatically identifying patient demands in Internet environments. METHODS This study used a combination of manual annotation and computer-aided method to mine value of 9936 posts collected from four OHCs in China. On one hand, we recruited 7 diabetes or depression medical experts to label text according to a theoretical framework, forming patient need theory in Internet environments, which is designed for the first two research goals. On the other hand, based on the corpus constructed by manual annotation, this research used Natural Language Processing (NLP) and Machine Learning (ML) to train a model for automatically identifying patient demands, which is planned to reach the third research purpose. RESULTS According to statistical results, the proportion of posts related to patient needs in OHCs was approximately 91%, and posts concerned with Emotional Support (18%), Information (28%) and Socialization (44%) needs were the top three most prevalent categories. However, when OHCs were divided according to user composition and disease type, patient needs were diverse: the chief demand was Socialization in Patient Interaction OHCs (65%), Diabetes OHCs (50%), and Depression OHCs (69%), while Information (96%) was the chief demand in Patient-Doctor Interaction OHCs. A model was trained to identify patient needs taking Linguistic Features (LF) and Category Keyword Features (CKF) as input and Random Forest as the classifier, of which the F1 value was higher than 0.80 on test set. CONCLUSIONS Patient needs in the Internet environment mainly include Emotional Support needs, Information needs and Socialization needs. Differences in community type and disease type can lead to diverse patient needs in OHCs. It is practical to use computer-aided methods to identify patient needs in OHCs automatically.