Objective To identify user needs on digital mental health platforms through text mining to guide service optimization. Methods User comments collected over 6 months via Python-based web scraping (ethically reviewed) underwent data cleaning, word segmentation, and stop-word removal. Latent topics were extracted using Latent Dirichlet Allocation (LDA), with sentiment and semantic network analyses via ROST Content Mining System (Version 6.0). A need-intensity formula integrated with LDA enabled quantitative analysis of user needs and emotional tendencies. Results From 25,131 valid user comments, six main topics were identified. Topic prevalence, measured by comment volume, was highest for “Career and Personal Growth” (20.40%) and “Social and Emotional Support” (20.15%). Sentiment analysis showed over 77% of comments expressed negative emotions, with each topic exceeding 96% negative sentiment. We then calculated a composite need-intensity score (via LDA integration and Analytic Hierarchy Process weighting: comment count 0.54, sentiment 0.16, user attention 0.30) to prioritize beyond mere prevalence. This analysis identified “Career and Personal Growth” (0.94) and “Social and Emotional Support” (0.88) as the most pressing needs. Conclusions Users exhibit pronounced needs in emotion management and psychological counseling, with a strong emphasis on familial and social support. To address the most urgent needs identified by our need-intensity analysis, we recommend that platforms implement targeted features such as a structured career stress triage pathway and secure family linkage options. These actionable strategies can enhance service precision and user engagement, providing a clear roadmap for optimizing digital mental health service delivery.
Wu et al. (Sun,) studied this question.