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TwitterApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
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Dataset Name: BBC Articles Sentiment Analysis Dataset
Source: BBC News
Description: This dataset consists of articles from the BBC News website, containing a diverse range of topics such as business, politics, entertainment, technology, sports, and more. The dataset includes articles from various time periods and categories, along with labels representing the sentiment of the article. The sentiment labels indicate whether the tone of the article is positive, negative, or neutral, making it suitable for sentiment analysis tasks.
Number of Instances: [Specify the number of articles in the dataset, for example, 2,225 articles]
Number of Features: 1. Article Text: The content of the article (string). 2. Sentiment Label: The sentiment classification of the article. The possible labels are: - Positive - Negative - Neutral
Data Fields: - id: Unique identifier for each article. - category: The category or topic of the article (e.g., business, politics, sports). - title: The title of the article. - content: The full text of the article. - sentiment: The sentiment label (positive, negative, or neutral).
Example: | id | category | title | content | sentiment | |----|-----------|---------------------------|-------------------------------------------------------------------------|-----------| | 1 | Business | "Stock Market Surge" | "The stock market has surged to new highs, driven by strong earnings..." | Positive | | 2 | Politics | "Election Results" | "The election results were a mixed bag, with some surprises along the way." | Neutral | | 3 | Sports | "Team Wins Championship" | "The team won the championship after a thrilling final match." | Positive | | 4 | Technology | "New Smartphone Release" | "The new smartphone release has received mixed reactions from users." | Negative |
Preprocessing Notes: - The text has been preprocessed to remove special characters and any HTML tags that might have been included in the original articles. - Tokenization or further text cleaning (e.g., lowercasing, stopword removal) may be necessary depending on the model and method used for sentiment classification.
Use Case: This dataset is ideal for training and evaluating machine learning models for sentiment classification, where the goal is to predict the sentiment (positive, negative, or neutral) based on the article's text.
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TwitterIn the year ending March 31, 2025, the BBC saw an income of approximately *** billion British pounds. Of this, **** billion British pounds were attributed to the license fees paid by UK households. The BBC is a cornerstone of the British TV industry, with BBC 1 being used weekly by roughly ** percent of the population. BBC iPlayer In July 2007, the BBC launched its on demand internet service, the iPlayer. Today, the platform is seeing weekly viewing times of over *** million minutes. Landmark series such as Blue Planet II, saw almost **** million requests on the platform for its first episode. Viewership The BBC is the most popular broadcaster in terms of viewers in the UK. In july 2025, the BBC had an audience share more than *** percent higher than the next largest broadcaster. The BBC 1 channel alone, had a quarterly reach of approximately ** million in the third quarter of 2019.
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TwitterApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Techsalerator's News Events Data for the United Kingdom: A Comprehensive Overview
Techsalerator's News Events Data for the United Kingdom provides a robust resource for businesses, researchers, and media organizations. This dataset aggregates information on major news events across the UK from various media sources, including news outlets, online publications, and social platforms. It offers valuable insights for those looking to track trends, analyze public sentiment, or monitor industry-specific developments.
Key Data Fields - Event Date: Records the exact date of the news event. Essential for analysts tracking trends over time or businesses reacting to market changes. - Event Title: A concise headline summarizing the event. Allows users to quickly categorize and evaluate news content based on relevance. - Source: Indicates the news outlet or platform reporting the event. Helps users gauge credibility and assess the event's reach and influence. - Location: Provides geographic details about where the event occurred within the UK. Useful for regional analysis or localized marketing strategies. - Event Description: Offers a detailed summary of the event, including key developments, participants, and potential impact. Important for understanding the context and implications.
Top 5 News Categories in the United Kingdom - Politics: Covers major news on government decisions, political movements, elections, and policy changes affecting the national landscape. - Economy: Focuses on economic indicators, inflation rates, international trade, and corporate activities impacting business and finance sectors. - Social Issues: Includes news on protests, public health, education, and other societal concerns driving public discourse. - Sports: Highlights events in football, cricket, and other popular sports, often generating widespread attention and engagement. - Technology and Innovation: Reports on tech developments, startups, and innovations in the UK’s tech sector, featuring emerging companies and advancements.
Top 5 News Sources in the United Kingdom - BBC News: A leading news outlet known for its comprehensive coverage of national and international news, including politics, economy, and social issues. - The Guardian: Provides in-depth reporting on a wide range of topics, including politics, culture, and current affairs. - Sky News: Offers breaking news updates and live coverage on major events across the UK and globally. - The Times: A well-established newspaper delivering detailed reports on politics, business, and social issues. - The Telegraph: Features extensive coverage of news, politics, and lifestyle topics, known for its analysis and commentary.
Accessing Techsalerator’s News Events Data for the United Kingdom To access Techsalerator’s News Events Data for the United Kingdom, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.
Included Data Fields - Event Date - Event Title - Source - Location - Event Description - Event Category (Politics, Economy, Sports, etc.) - Participants (if applicable) - Event Impact (Social, Economic, etc.)
Techsalerator’s dataset is an invaluable tool for tracking significant events in the United Kingdom. It supports informed decision-making, whether for business strategy, market analysis, or academic research, providing a clear view of the country’s news landscape.
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TwitterAn investigation found that coverage of the Russia-Ukraine war accounted for the majority of lead stories in several news brands in the United Kingdom from May to August 2023. A total of ** percent of lead stories from BBC News covered the Ukraine war as a topic, while it held a share of ** percent at the Telegraph. Likewise, the war was the leading topic for lead stories at Independent, Sky News, and The Times. However, the lead stories at the Daily Mail and the Mirror mostly covered Lucy Letby and Philip Schofield.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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UK Gas fell to 72.60 GBp/thm on December 2, 2025, down 1.67% from the previous day. Over the past month, UK Gas's price has fallen 11.75%, and is down 40.33% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. UK Natural Gas - values, historical data, forecasts and news - updated on December of 2025.
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TwitterApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
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Dataset Name: BBC Articles Sentiment Analysis Dataset
Source: BBC News
Description: This dataset consists of articles from the BBC News website, containing a diverse range of topics such as business, politics, entertainment, technology, sports, and more. The dataset includes articles from various time periods and categories, along with labels representing the sentiment of the article. The sentiment labels indicate whether the tone of the article is positive, negative, or neutral, making it suitable for sentiment analysis tasks.
Number of Instances: [Specify the number of articles in the dataset, for example, 2,225 articles]
Number of Features: 1. Article Text: The content of the article (string). 2. Sentiment Label: The sentiment classification of the article. The possible labels are: - Positive - Negative - Neutral
Data Fields: - id: Unique identifier for each article. - category: The category or topic of the article (e.g., business, politics, sports). - title: The title of the article. - content: The full text of the article. - sentiment: The sentiment label (positive, negative, or neutral).
Example: | id | category | title | content | sentiment | |----|-----------|---------------------------|-------------------------------------------------------------------------|-----------| | 1 | Business | "Stock Market Surge" | "The stock market has surged to new highs, driven by strong earnings..." | Positive | | 2 | Politics | "Election Results" | "The election results were a mixed bag, with some surprises along the way." | Neutral | | 3 | Sports | "Team Wins Championship" | "The team won the championship after a thrilling final match." | Positive | | 4 | Technology | "New Smartphone Release" | "The new smartphone release has received mixed reactions from users." | Negative |
Preprocessing Notes: - The text has been preprocessed to remove special characters and any HTML tags that might have been included in the original articles. - Tokenization or further text cleaning (e.g., lowercasing, stopword removal) may be necessary depending on the model and method used for sentiment classification.
Use Case: This dataset is ideal for training and evaluating machine learning models for sentiment classification, where the goal is to predict the sentiment (positive, negative, or neutral) based on the article's text.