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We have successfully extracted a comprehensive news dataset from CNBC, covering not only financial updates but also an extensive range of news categories relevant to diverse audiences in Europe, the US, and the UK. This dataset includes over 500,000 records, meticulously structured in JSON format for seamless integration and analysis.
This extensive extraction spans multiple segments, such as:
Each record in the dataset is enriched with metadata tags, enabling precise filtering by region, sector, topic, and publication date.
The comprehensive news dataset provides real-time insights into global developments, corporate strategies, leadership changes, and sector-specific trends. Designed for media analysts, research firms, and businesses, it empowers users to perform:
Additionally, the JSON format ensures easy integration with analytics platforms for advanced processing.
Looking for a rich repository of structured news data? Visit our news dataset collection to explore additional offerings tailored to your analysis needs.
To get a preview, check out the CSV sample of the CNBC economy articles dataset.
F CNBC International is a leading global financial news and information services company, providing unparalleled insights and analysis to business stakeholders around the world. Founded in 2005, the company has established itself as a trusted authority in the realm of financial news, broadcasting, and online content.
With a focus on coverage of global markets, financial trends, and economic developments, F CNBC International offers a broad range of data and information on companies, industries, and markets worldwide. From stock prices and trading data to market research and economic indicators, the company's platform provides a comprehensive and easily accessible array of financial resources for professionals and individuals alike, offering unparalleled insight into the world of global finance.
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
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Context Scraped from CNBC, the Guardian, and Reuters official websites, the headlines in these datasets reflects the overview of the U.S. economy and stock market every day for the past year to 2 years.
Content Data scraped from CNBC contains the headlines, last updated date, and the preview text of articles from the end of December 2017 to July 19th, 2020. Data scraped from the Guardian Business contains the headlines and last updated date of articles from the end of December 2017 to July 19th, 2020 since the Guardian Business does not offer preview text. Data scraped from Reuters contains the headlines, last updated date, and the preview text of articles from the end of March 2018 to July 19th, 2020. Inspiration I firmly believe that the sentiment of financial news articles reflects and directs the performance of the U.S. stock market. Therefore, by applying Natural Language Processing (NLP) through these headlines, I can see how the positivity/negativity of the score through each day correlate to the stock market's gains/losses.
The cover image was taken from https://hipwallpaper.com/stock-trader-wallpapers/
Original Data Source: Financial News Headlines Data
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Graph and download economic data for 30-Day Average SOFR (SOFR30DAYAVG) from 2018-05-02 to 2025-07-11 about 1-month, financing, overnight, average, securities, and USA.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The NewMinerCollection was built by collecting news, in the English language, from The Guardian, CNN, BBC, Fox News, NyPost, China Daily and CNBC websites, from 1990 until 2016 using a web crawler. This dataset contains 7000 news items equally distributed among the seven categories:
id_category category 1 Arts, Culture & Entertainment 4 Economy, Business & Finance 6 Environmental Issues 10 Lifestyle & Leisure 11 Politics 15 Sport 13 Science & Technology
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We have successfully extracted a comprehensive news dataset from CNBC, covering not only financial updates but also an extensive range of news categories relevant to diverse audiences in Europe, the US, and the UK. This dataset includes over 500,000 records, meticulously structured in JSON format for seamless integration and analysis.
This extensive extraction spans multiple segments, such as:
Each record in the dataset is enriched with metadata tags, enabling precise filtering by region, sector, topic, and publication date.
The comprehensive news dataset provides real-time insights into global developments, corporate strategies, leadership changes, and sector-specific trends. Designed for media analysts, research firms, and businesses, it empowers users to perform:
Additionally, the JSON format ensures easy integration with analytics platforms for advanced processing.
Looking for a rich repository of structured news data? Visit our news dataset collection to explore additional offerings tailored to your analysis needs.
To get a preview, check out the CSV sample of the CNBC economy articles dataset.