As the economy and society develop and the standard of living improves, people’s health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories of user information requirements: causes, symptoms and manifestations, examination and diagnosis, treatment, self-management and regulation, impact, prevention, social life, and knowledge acquisition. By analyzing the distribution of positive and negative sentiments across each topic, the correlation between various health information demands and emotional expressions is investigated. The model established in this paper integrates BERT’s semantic comprehension with LDA’s topic modeling capabilities, enhancing the accuracy of topic identification and sentiment analysis while providing a more comprehensive evaluation of user information demands. This research furthers our understanding of users’ emotional reactions and presents valuable insights for delivering personalized health information in online communities.

This study examines the determinants that drive the behavior of sharing health information within online health communities. Leveraging the Theory of Planned Behavior, the Technology Acceptance Model, and the “Knowledge-Attitude-Practice” theory, a comprehensive model elucidating the key elements that sway the health information-sharing behavior among users of online health communities is designed. This model is validated through Structural Equation Modeling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Findings derived from the SEM suggest that perceived ease of use, perceived usefulness, perceived trust, and perceived behavioral control exert a significant positive impact on attitudes towards health information sharing, the intention to share health information, and the actual health information-sharing behavior. The fsQCA unfolds two unique configuration path models that lead to the emergence of health information-sharing behavior: one predicated on perceived trust and sharing intention, and the other on perceived usefulness, behavioral control, and sharing attitude. This research provides invaluable insights, fostering a deeper comprehension of the dynamics involved in health information sharing within online communities, thereby directing the design of more effective health platforms to augment user engagement and enable informed health decisions.

Disruptive technologies are related to a country’s competitiveness and international status. Accurately identifying and predicting the trends in disruptive technologies through scientific methods can effectively grasp the dynamics of technological development, adjust the national science and technology strategic layout, and better seize the high ground in international competition. Based on patent text data, this paper uses the improved LDA2Vec model combined with relevant indicators to identify the main topics in disruptive technologies, and predicts and analyzes the development trend through the establishment of an ARIMA model. Taking the energy technology field as an example, the main topics and development trends concerning disruptive technologies in this field are obtained. The study found that ten technologies, including energy storage technology, energy internet management technology, and offshore wind energy technology, are disruptive technologies in the energy technology field, and the development speed of energy storage technology is the fastest. To verify the correctness of the conclusion, this paper compares the results with artificial verification methods such as expert interviews and document verification, and finds that the two are basically consistent, thus verifying the effectiveness and feasibility of the proposed method.