An increasing number of the renowned company’s investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag‐of‐words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.

Introduction: Mental health issues bring a heavy burden to individuals and societies around the world. Recently, the large language model ChatGPT has demonstrated potential in depression intervention. The primary objective of this study was to ascertain the viability of ChatGPT as a tool for aiding counselors in their interactions with patients while concurrently evaluating its comparability to human-generated content (HGC). Methods: We propose a novel framework that integrates state-of-the-art AI technologies, including ChatGPT, BERT, and SHAP, to enhance the accuracy and effectiveness of mental health interventions. ChatGPT generates responses to user inquiries, which are then classified using BERT to ensure the reliability of the content. SHAP is subsequently employed to provide insights into the underlying semantic constructs of the AI-generated recommendations, enhancing the interpretability of the intervention. Results: Remarkably, our proposed methodology consistently achieved an impressive accuracy rate of 93.76%. We discerned that ChatGPT always employs a polite and considerate tone in its responses. It refrains from using intricate or unconventional vocabulary and maintains an impersonal demeanor. These findings underscore the potential significance of AIGC as an invaluable complementary component in enhancing conventional intervention strategies.Discussion: This study illuminates the considerable promise offered by the utilization of large language models in the realm of healthcare. It represents a pivotal step toward advancing the development of sophisticated healthcare systems capable of augmenting patient care and counseling practices.