4 datasets found
  1. Z

    Forex News Annotated Dataset for Sentiment Analysis

    • data.niaid.nih.gov
    Updated Nov 11, 2023
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    Georgios Fatouros; Kalliopi Kouroumali (2023). Forex News Annotated Dataset for Sentiment Analysis [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7976207
    Explore at:
    Dataset updated
    Nov 11, 2023
    Dataset provided by
    Hellenic Telecommunications Organisation S.A.
    University of Piraeus
    Authors
    Georgios Fatouros; Kalliopi Kouroumali
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

    To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

    We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

    Examples of Annotated Headlines
    
    
        Forex Pair
        Headline
        Sentiment
        Explanation
    
    
    
    
        GBPUSD 
        Diminishing bets for a move to 12400 
        Neutral
        Lack of strong sentiment in either direction
    
    
        GBPUSD 
        No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
        Positive
        Positive sentiment towards GBPUSD (Cable) in the near term
    
    
        GBPUSD 
        When are the UK jobs and how could they affect GBPUSD 
        Neutral
        Poses a question and does not express a clear sentiment
    
    
        JPYUSD
        Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
        Positive
        Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply
    
    
        USDJPY
        Dollar rebounds despite US data. Yen gains amid lower yields 
        Neutral
        Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other
    
    
        USDJPY
        USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
        Negative
        USDJPY is expected to reach a lower value, with the USD losing value against the JPY
    
    
        AUDUSD
    

    RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

        Positive
        Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.
    

    Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

  2. A

    The Changing Australian, 1983; Leaders' Study

    • dataverse.ada.edu.au
    pdf, rtf, txt, zip
    Updated May 24, 2019
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    Terence W. Beed; Terence W. Beed (2019). The Changing Australian, 1983; Leaders' Study [Dataset]. http://doi.org/10.26193/1FBYYY
    Explore at:
    pdf(315632), zip(31748), zip(23750), pdf(122529), pdf(892146), zip(28593), pdf(1327017), pdf(242262), rtf(1119040), txt(142218), zip(16171)Available download formats
    Dataset updated
    May 24, 2019
    Dataset provided by
    ADA Dataverse
    Authors
    Terence W. Beed; Terence W. Beed
    License

    https://dataverse.ada.edu.au/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.26193/1FBYYYhttps://dataverse.ada.edu.au/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.26193/1FBYYY

    Area covered
    Australia
    Description

    The 'changing Australian' study sought information on the workforce, confidence in the ability of various leadership groups, the role of government in a changing society, and the quality of life. This data set is one part of this study, and includes responses from leaders in business, government and trade unions. The other part (see SSDA Study No. 155) uses a matched questionnaire and includes responses from adults in the workforce. Variables in this data set include attitudes on seriousness of Australia's problems, confidence in Australia's economic future, identification with Australia, confidence in Australian institutions, management capability of top people, priorities for Federal Government, cause of and reducing unemployment, retrenching workers, inflation, power of trade unions/ big business/ Federal Government/ mass media, cooperation between government/ business/ labour, government spending and social welfare benefits, tax avoidance, tax rates, direct vs indirect taxation, taxation of lump sums, work-related benefits, attitudes to work, school education as a preparation for life, quality of life, Australians' perception of overseas attitudes to Australia and Australians, whether well informed about public issues, commitment to ideas and causes, and working wives. Background variables are interest in politics, level of education, age group, sex, and occupation type of 'leader'.

  3. A

    The Changing Australian, 1983; Workforce Survey

    • dataverse.ada.edu.au
    pdf, rtf, txt, zip
    Updated May 24, 2019
    Share
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    Terence W. Beed; I. W. McNair; Terence W. Beed; I. W. McNair (2019). The Changing Australian, 1983; Workforce Survey [Dataset]. http://doi.org/10.26193/OXRZKA
    Explore at:
    txt(182427), zip(115676), zip(113530), rtf(1346113), pdf(1125507), zip(79445), pdf(172843), zip(75003)Available download formats
    Dataset updated
    May 24, 2019
    Dataset provided by
    ADA Dataverse
    Authors
    Terence W. Beed; I. W. McNair; Terence W. Beed; I. W. McNair
    License

    https://dataverse.ada.edu.au/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.26193/OXRZKAhttps://dataverse.ada.edu.au/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.26193/OXRZKA

    Area covered
    Australia
    Description

    The 'changing Australian' study sought information on the workforce, confidence in the ability of various leadership groups, the role of government in a changing society, and the quality of life. This data set is one part of this study, and includes responses from adults in the workforce. The other part (SSDA Study No. 156) uses a matched questionnaire and includes responses from leaders in business, government and trade unions. Variables in this data set include coping with everyday living, financial situation of household, seriousness of Australia's problems, confidence in Australia's economic future, identification with Australia, confidence in Australian institutions, management capability of top people, priorities for Federal Government, cause of and reducing unemployment, retrenching workers, inflation, power of trade unions/ big business/ Federal Government/ mass media, cooperation between government/ business/ labour, government spending and social welfare benefits, tax avoidance, tax rates, direct vs indirect taxation, taxation of lump sums, work-related benefits, whether actively seeking employment, attitudes to work, school education as a preparation for life, quality of life, holidays and travel, Australians' perceptions of overseas attitudes to Australia and Australians, whether well informed about public issues, commitment to ideas and causes, and working wives. Background variables are country of birth, length of current employment, occupation, whether work part-time or full-time, trade union membership, interest in politics, marital status, level of education, personal gross income, age group, sex and whether housewife, and telephone ownership.

  4. T

    Wheat - Price Data

    • tradingeconomics.com
    • pl.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Nov 24, 2025
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    TRADING ECONOMICS (2025). Wheat - Price Data [Dataset]. https://tradingeconomics.com/commodity/wheat
    Explore at:
    csv, json, excel, xmlAvailable download formats
    Dataset updated
    Nov 24, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Sep 21, 1977 - Dec 1, 2025
    Area covered
    World
    Description

    Wheat fell to 529.25 USd/Bu on December 1, 2025, down 0.33% from the previous day. Over the past month, Wheat's price has fallen 2.62%, and is down 1.53% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on December of 2025.

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Share
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TwitterTwitter
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Click to copy link
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Close
Cite
Georgios Fatouros; Kalliopi Kouroumali (2023). Forex News Annotated Dataset for Sentiment Analysis [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7976207

Forex News Annotated Dataset for Sentiment Analysis

Explore at:
Dataset updated
Nov 11, 2023
Dataset provided by
Hellenic Telecommunications Organisation S.A.
University of Piraeus
Authors
Georgios Fatouros; Kalliopi Kouroumali
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

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