CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This dataset features financial news headlines collected from leading financial news websites, including CNBC, The Guardian, and Reuters. It provides an overview of the U.S. economy and stock market, primarily reflecting daily market sentiment over several years. The main purpose of this dataset is to facilitate Natural Language Processing (NLP) analyses to explore the correlation between the positivity or negativity of news sentiment and U.S. stock market performance, such as gains and losses. It is ideal for data scientists and analysts keen on understanding market dynamics through textual data.
The dataset typically includes the following columns, though availability may vary slightly by source: * Headlines: The main title or headline of the financial article. * Time: The last updated date and time of the article. * Description: A preview or summary text of the article's content.
The data files are generally provided in CSV format. Specific numbers for rows or records are not available within the provided sources, but the dataset is structured to allow for easy processing and analysis.
This dataset is well-suited for a variety of applications, including: * Sentiment analysis of financial news to predict market movements. * Developing and testing Natural Language Processing (NLP) models. * Data science and analytics projects focused on economic trends and stock market performance. * Research into the impact of media on financial markets.
The dataset covers news related to the U.S. economy and stock market. * Time Range: * CNBC and The Guardian data spans from late December 2017 to 19th July 2020. * Reuters data covers from late March 2018 to 19th July 2020. * Collectively, the headlines reflect an overview of the U.S. economy and stock market for approximately one to two years from their scraping date.
CCO
This dataset is intended for a range of users, including: * Data Scientists and Analysts performing market sentiment analysis. * Researchers studying economic indicators and financial news impact. * Individuals interested in Natural Language Processing (NLP) and text analysis applications in finance. * Anyone looking to gain insights into the relationship between news sentiment and stock market performance.
Original Data Source: Financial News Headlines Data
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Oil prices saw a modest rise after declines due to Saudi production signals, amidst U.S. economic contraction and trade policy impacts.
https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/
Global book publishers have had their fair share of ups and downs stemming from volatile economic conditions and changing consumer preferences. The pandemic slowed physical print releases as supply chains were disrupted and publishers were forced to operate at a limited capacity. While physical bookstores were closed, online retailers could still deliver books directly to consumers' doorsteps, preventing publishers from facing a significant drop-off. Even so, smaller publishers that couldn't keep up were forced to exit because of low profitability. As the effects of the pandemic waned, book publishers faced higher paper prices, causing an uptick in the prices they charged consumers. Still, rising reading activity carried over and consumers were out and about in physical bookstores. Revenue has pushed up at a CAGR of 0.3% through the end of 2024, reaching $151.9 billion, including a 1.1% uptick in 2024 alone. The transition to digital media will continue to impact the way consumers read. The rise of e-books is forcing publishers to adapt and make their books more accessible digitally. While digital books are more condiment, smaller print runs will cause publishers to pay more for each printing, eating into profit and offsetting the costs they would save by going digital. Even so, many publishers are leveraging technology to make physical books more accessible than ever. Publisher websites can provide links to retailers, release dates and prices for anything a customer wants. This has helped stave off some digital penetration as shopping at brick-and-mortar locations has rebounded since the pandemic. Emerging markets are set to push new publishers into the industry as countries worldwide expand their literacy rates. The push for education alongside local government funding will pave the way for new publishers to release books that cater to these countries, facilitating academic book sales. Overall, global book publishing revenue is set to expand at a CAGR of 2.2% to $169.4 billion through the end of 2029.
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CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This dataset features financial news headlines collected from leading financial news websites, including CNBC, The Guardian, and Reuters. It provides an overview of the U.S. economy and stock market, primarily reflecting daily market sentiment over several years. The main purpose of this dataset is to facilitate Natural Language Processing (NLP) analyses to explore the correlation between the positivity or negativity of news sentiment and U.S. stock market performance, such as gains and losses. It is ideal for data scientists and analysts keen on understanding market dynamics through textual data.
The dataset typically includes the following columns, though availability may vary slightly by source: * Headlines: The main title or headline of the financial article. * Time: The last updated date and time of the article. * Description: A preview or summary text of the article's content.
The data files are generally provided in CSV format. Specific numbers for rows or records are not available within the provided sources, but the dataset is structured to allow for easy processing and analysis.
This dataset is well-suited for a variety of applications, including: * Sentiment analysis of financial news to predict market movements. * Developing and testing Natural Language Processing (NLP) models. * Data science and analytics projects focused on economic trends and stock market performance. * Research into the impact of media on financial markets.
The dataset covers news related to the U.S. economy and stock market. * Time Range: * CNBC and The Guardian data spans from late December 2017 to 19th July 2020. * Reuters data covers from late March 2018 to 19th July 2020. * Collectively, the headlines reflect an overview of the U.S. economy and stock market for approximately one to two years from their scraping date.
CCO
This dataset is intended for a range of users, including: * Data Scientists and Analysts performing market sentiment analysis. * Researchers studying economic indicators and financial news impact. * Individuals interested in Natural Language Processing (NLP) and text analysis applications in finance. * Anyone looking to gain insights into the relationship between news sentiment and stock market performance.
Original Data Source: Financial News Headlines Data