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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. [ABSTRACT FROM AUTHOR]

Analysts ask managers for forward-looking information in one-third of quarterly conference calls; most frequently, seeking earnings per share guidance. In this study, we examine the long-term consequences of analysts' questions on future manager disclosure choices. Using six types of commonly provided guidance, we find that when analysts request new guidance or ask about prior guidance, managers are more likely to provide similar guidance in future quarters. When analysts do not ask about prior guidance, managers are less likely to provide that type of guidance in the future. This effect is long-lasting, spills over into earnings announcements, and is strongest for firms with the most severe information problems. Our results provide evidence that analysts shape managers' disclosure choices in meaningful ways. [ABSTRACT FROM AUTHOR]

This paper considers the properties of using the multidimensional approach in the information support of scientific research on ethnology and historical anthropology based on the information resources of the Institute of Scientific Information on Social Sciences (INION). The principles of the content processing of documents and queries based on a complex of linguistic tools of the Automated Information System on Social Sciences (AISON) are considered. The problems of increasing the professional competence and level of information culture of consumers who search for ethnological information by queries of different complexities are discussed. [ABSTRACT FROM AUTHOR]