Unlock the full potential of BBC broadcast data with our comprehensive dataset featuring transcripts, program schedules, headlines, topics, and multimedia resources. This all-in-one dataset is designed to empower media analysts, researchers, journalists, and advocacy groups with actionable insights for media analysis, transparency studies, and editorial assessments.

Dataset Features

Transcripts: Access detailed broadcast transcripts, including headlines, content, author details, and publication dates. Perfect for analyzing media framing, topic frequency, and news narratives across various programs. Program Schedules: Explore program schedules with accurate timing, show names, and related metadata to track news coverage patterns and identify trends. Topics and Keywords: Analyze categorized topics and keywords to understand content diversity, editorial focus, and recurring themes in news broadcasts. Multimedia Content: Gain access to videos, images, and related articles linked to each broadcast for a holistic understanding of the news presentation. Metadata: Includes critical data points like publication dates, last updates, content URLs, and unique IDs for easier referencing and cross-analysis.

Customizable Subsets for Specific Needs Our CNN dataset is fully customizable to match your research or analytical goals. Focus on transcripts for in-depth media framing analysis, extract multimedia for content visualization studies, or dive into program schedules for broadcast trend analysis. Tailor the dataset to ensure it aligns with your objectives for maximum efficiency and relevance.

Popular Use Cases

Media Analysis: Evaluate news framing, content diversity, and topic coverage to assess editorial direction and media focus. Transparency Studies: Analyze journalistic standards, corrections, and retractions to assess media integrity and accountability. Audience Engagement: Identify recurring topics and trends in news content to understand audience preferences and behavior. Market Analysis: Track media coverage of key industries, companies, and topics to analyze public sentiment and industry relevance. Journalistic Integrity: Use transcripts and metadata to evaluate adherence to reporting practices, fairness, and transparency in news coverage. Research and Scholarly Studies: Leverage transcripts and multimedia to support academic studies in journalism, media criticism, and political discourse analysis.

Whether you are evaluating transparency, conducting media criticism, or tracking broadcast trends, our BBC dataset provides you with the tools and insights needed for in-depth research and strategic analysis. Customize your access to focus on the most relevant data points for your unique needs.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

BBC Stock: A High-Growth Company with a Bright Future

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Explore the booming Daily Newsletters market, driven by evolving news consumption and digital trends. Discover key insights, market size, growth drivers, and top companies shaping the future of curated news delivery.

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.

Three news sources have been used in creating this dataset. 1. Sun, J. (2016, August). Daily News for Stock Market Prediction, Version 1. Retrieved (2024, August) from https://www.kaggle.com/aaron7sun/stocknews. 2. ARYAN SINGH. NYT Articles: 2.1M+ (2000-Present) Daily Updated. https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-present. 3. GABRIEL PREDA. BBC News. https://www.kaggle.com/datasets/gpreda/bbc-news.

The first source covers from 2008-06-08 to 2016-07-01, the top 25 news of each day from Reddit World News. The second source is a direct import of the abstract column from New York Times articles from 2016-07-01 to 2017-09-05. The third is also a direct import of the description column from BBC News from 2017-09-05 to 2024-08-03. Thus, the whole coverage is from 2008-06-08 to 2024-08-03.

Three models have been used for sentiment results. NLTK VADER is applied first as it is the most lightweight and fastest to apply on large amounts of data. But, as news is mostly neural, NLTK vader gave a 1.0 neutral score for around 25% of the data. Therefore, two more advanced models, NLTK RoBERTa and HUGGING FACE distilbert-base-uncased-finetuned-sst-2-english, are applied to these neutral articles to identify them accurately.

Part of my school project for Nanyang Polytechnic | AI & Data Engineering

The global news app market is experiencing robust growth, driven by increasing smartphone penetration, readily available high-speed internet, and a rising demand for personalized and on-the-go news consumption. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. Key growth drivers include the increasing adoption of subscription models by news publishers, offering premium content and ad-free experiences, as well as sophisticated algorithms providing personalized news feeds. Furthermore, advancements in artificial intelligence (AI) are enabling enhanced features like real-time news alerts, curated content based on user preferences, and advanced search functionalities. The market is segmented by application (subscription services and advertising) and by type (Android, iOS, web app, and others), with significant variations in revenue generation and user base across these categories. Competition is intense, with established tech giants like Apple, Google, and Microsoft vying for market share alongside specialized news providers such as The New York Times, BBC, and CNN, and emerging players leveraging social media integration and innovative content formats. Geographic distribution shows North America and Europe currently dominating the market, but significant growth potential lies within the Asia-Pacific region, driven primarily by the expanding digital landscape and increasing internet penetration in countries like India and China. Market restraints include concerns regarding data privacy and security, the spread of misinformation and “fake news,” and the evolving challenges of maintaining a sustainable revenue model in a fiercely competitive environment. The industry faces ongoing challenges in monetizing user engagement and balancing user experience with data collection practices. Future growth will likely depend on the ability of news apps to adapt to evolving user preferences, enhance their features through AI and machine learning, effectively address misinformation, and successfully navigate the complexities of data privacy regulations. The ongoing development of personalized news experiences, coupled with innovative subscription models and strategic partnerships, will be crucial for sustained success in this dynamic market.

Brent fell to 63.05 USD/Bbl on December 2, 2025, down 0.19% from the previous day. Over the past month, Brent's price has fallen 2.84%, and is down 14.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Brent crude oil - values, historical data, forecasts and news - updated on December of 2025.

Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Japan_25_Portfolios_MV_PTBV: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_25_Portfolios_MV_PE: Monthly returns for 25 size-PE portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Japan_50_Portfolios_SECTOR: Monthly returns for 50 industry portfolios. (Raw data source: Datastream database)
4. Japan_3 Factors: Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Japan_5 Factors: Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database)
6. Japan_NUCLEAR_Y: Instrument in years with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
7. Japan_NUCLEAR_M: Instrument in months with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
8. Japan_RF_M: Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Company data: Names and general data of the companies that constitute the sample. (Raw data source: Datastream database)
10. Number of stocks in portfolios: Number of stocks included each year in Japan_25_Portfolios_MV_PTBV, Japan_25_Portfolios_MV_PE and Japan_50_Portfolios_SECTOR. (Raw data source: Datastream database)

We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-earnings ratio (PE series), and (vii) industry (SECTOR series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations. Accordingly, our sample comprises a total number of 5,212 stocks.

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

According to Cognitive Market Research, The Global Process Safety Services market size was USD 16.8 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 10.80% from 2023 to 2030. What are the Most Significant Opportunities and Factors Influencing the Process Safety Services Market?

Rising of Industrial Accidents Drives the Market Expansion

The increasing incidence of big and minor industrial accidents has heightened awareness about the necessity of process safety. Companies are investing in safety measures to avoid accidents, save downtime, and maintain a favorable public image because contemporary enterprises are more concerned with delivering maximum outcomes and manufacturing efficiency than with producing safely; the frequency of accidents and catastrophic incidents has grown over time. Several variables, each dependent on the context, commonly cause accidents.

For instance, according to data from BBC News, industrial accidents kill hundreds of individuals yearly and severely handicap thousands more. In 2021, a federal minister told parliament that over the previous five years, at least 6,500 employees had died while working in factories, ports, mines, and construction sites. Labor campaigners with years of experience in the sector told the BBC that the statistics may be higher because many events are not reported or documented.

(Source:www.bbc.com/news/world-asia-india-62631699)

Because Europe is a highly industrialized region that might be used as a case study to illustrate global industrialization, the statistics could imply a global trend in industrial accidents. Nonetheless, as workplace mishaps increase, the demand for a medium to reduce accidents grows more pressing. The respective factors will drive the process safety market.

Improving Factory Management and Product Efficiency is Becoming Increasingly Important

Process safety services help manage the integrity of hazardous material handling operational systems and processes. It can aid in detecting, comprehending, managing, and preventing process-related problems. If an event happens during the production process, it can have a negative impact on the manufacturing process and product efficiency. In an accident, the product may leak or be damaged. However, by implementing process safety solutions, product loss may be reduced, and industrial efficiency can be increased, leading to rapid growth in the process safety services market.

According to a Manufacturing Institute report, one of the Biggest Causes of supply chain disruptions is insufficient production planning, which leads to increased costs and delayed delivery. 74% of firms reported at least one supply chain interruption the previous year, with 39% directly attributing it to inadequate production planning.

(Source:www.deskera.com/blog/effective-production-planning-manufacturing/)

The Factors Are Limiting the Growth Of The Web Hosting Services Market

Budget allocations and a lack of competent labor Limit Market Growth

Among the key market restrictions are inadequate budget allocation mechanisms and a lack of competent labor. Labor skills are critical in guaranteeing the safety of any industrial process, regardless of its risks. As a result, a lack of trained labor may increase the number of accidents. Furthermore, small and medium-sized organizations' budgets for critical safety do not contain expenditures for precise and fast incident monitoring. As a result, the budget scope may not be able to handle information security, technology, and workplace health properly, resulting in competing goals and a lack of collaboration. As a result, there is a scarcity of experienced labor and a lack of budget allocation for safety process management in small to medium-sized businesses.

Impact of COVID-19 on the Process Safety Services Market

Most sectors throughout the world have been badly impacted in recent months. This is due to major interruptions in their separate manufacturing and supply-chain activities caused by different precautionary lockdowns and other limitations imposed by regulatory bodies throughout the world. Furthermore, consumer demand has reduced as individuals are now more focused on minimizing non-essential expenses from their separate budgets since the general economic state of most people has been negatively impacted by this pandemic.

The COVID-19 pandemic and global...

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.

Discover the booming Science App market! Explore key trends, growth projections (CAGR 15%), leading companies, and regional insights in this comprehensive market analysis. Learn how AR/VR, gamification, and personalized learning are shaping the future of science education and information access.