The paper introduces FNSPID, a comprehensive financial news dataset that integrates stock prices and time-aligned financial news records for 4,775 S&P 500 companies from 1999 to 2023. The dataset aims to address the lack of extensive datasets that combine quantitative and qualitative sentiment analyses in financial market predictions. FNSPID is designed to enhance the accuracy of stock market predictions by incorporating both numerical data and sentiment information. The authors demonstrate that the dataset's size and quality significantly improve market prediction accuracy, and adding sentiment scores modestly enhances performance on transformer-based models. They also provide reproducible procedures for updating the dataset. The paper discusses the construction of FNSPID, its properties, and its applications in financial research, including sentiment analysis, trend evaluation, and risk assessment. The dataset is available for public use, offering valuable resources for researchers and practitioners in financial modeling and analysis.The paper introduces FNSPID, a comprehensive financial news dataset that integrates stock prices and time-aligned financial news records for 4,775 S&P 500 companies from 1999 to 2023. The dataset aims to address the lack of extensive datasets that combine quantitative and qualitative sentiment analyses in financial market predictions. FNSPID is designed to enhance the accuracy of stock market predictions by incorporating both numerical data and sentiment information. The authors demonstrate that the dataset's size and quality significantly improve market prediction accuracy, and adding sentiment scores modestly enhances performance on transformer-based models. They also provide reproducible procedures for updating the dataset. The paper discusses the construction of FNSPID, its properties, and its applications in financial research, including sentiment analysis, trend evaluation, and risk assessment. The dataset is available for public use, offering valuable resources for researchers and practitioners in financial modeling and analysis.