11 datasets found
  1. Z

    Forex News Annotated Dataset for Sentiment Analysis

    • data.niaid.nih.gov
    • paperswithcode.com
    • +1more
    Updated Nov 11, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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
    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. Penetration rate of online banking in Australia 2014-2029

    • statista.com
    Updated Nov 18, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista Research Department (2024). Penetration rate of online banking in Australia 2014-2029 [Dataset]. https://www.statista.com/topics/5759/banking-industry-in-australia/
    Explore at:
    Dataset updated
    Nov 18, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Australia
    Description

    The online banking penetration rate in Australia was forecast to continuously increase between 2024 and 2029 by in total 4.1 percentage points. After the fifteenth consecutive increasing year, the online banking penetration is estimated to reach 71.28 percent and therefore a new peak in 2029. Notably, the online banking penetration rate of was continuously increasing over the past years.Shown is the estimated percentage of the total population in a given region or country, which makes use of online banking.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

  3. T

    Australia 3-Month Bank Bill Swap Rate

    • tradingeconomics.com
    • ru.tradingeconomics.com
    • +16more
    csv, excel, json, xml
    Updated Sep 21, 2018
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    TRADING ECONOMICS (2018). Australia 3-Month Bank Bill Swap Rate [Dataset]. https://tradingeconomics.com/australia/bank-bill-swap-rate
    Explore at:
    json, csv, excel, xmlAvailable download formats
    Dataset updated
    Sep 21, 2018
    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
    Jan 3, 2023 - Mar 26, 2025
    Area covered
    Australia
    Description

    Bank Bill Swap Rate in Australia remained unchanged at 4.11 percent on Wednesday March 26. This dataset includes a chart with historical data for Australia Bank Bill Swap Rate.

  4. T

    Australia Inflation Expectations

    • tradingeconomics.com
    • de.tradingeconomics.com
    • +16more
    csv, excel, json, xml
    Updated Feb 13, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    TRADING ECONOMICS (2025). Australia Inflation Expectations [Dataset]. https://tradingeconomics.com/australia/inflation-expectations
    Explore at:
    excel, json, csv, xmlAvailable download formats
    Dataset updated
    Feb 13, 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
    Jan 31, 1995 - Mar 31, 2025
    Area covered
    Australia
    Description

    Inflation Expectations in Australia decreased to 3.60 percent in March from 4.60 percent in February of 2025. This dataset provides - Australia Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar and news.

  5. d

    SoE2017: Extent and rate of change of protected areas

    • data.gov.au
    • data.qld.gov.au
    • +1more
    csv
    Updated Oct 7, 2019
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Environment and Science (2019). SoE2017: Extent and rate of change of protected areas [Dataset]. https://data.gov.au/dataset/ds-qld-473c886f-a9c2-425d-8c36-3e3e4b07dfc5
    Explore at:
    csvAvailable download formats
    Dataset updated
    Oct 7, 2019
    Dataset provided by
    Environment and Science
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    The protected area estate increased by half a million hectares between 2015–2017 and now covers approximately 8.2% of Queensland The protected area estate increased by half a million hectares between 2015–2017 and now covers approximately 8.2% of Queensland

  6. A

    Australia Lending Rate: Personal Loans: Unsecured Term Loans: Fixed

    • ceicdata.com
    Updated Jan 15, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2025). Australia Lending Rate: Personal Loans: Unsecured Term Loans: Fixed [Dataset]. https://www.ceicdata.com/en/australia/lending-rate/lending-rate-personal-loans-unsecured-term-loans-fixed
    Explore at:
    Dataset updated
    Jan 15, 2025
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Mar 1, 2019 - Feb 1, 2020
    Area covered
    Australia
    Variables measured
    Lending Rate
    Description

    Australia Lending Rate: Personal Loans: Unsecured Term Loans: Fixed data was reported at 12.464 % pa in Feb 2020. This records an increase from the previous number of 12.104 % pa for Jan 2020. Australia Lending Rate: Personal Loans: Unsecured Term Loans: Fixed data is updated monthly, averaging 13.500 % pa from Dec 1988 (Median) to Feb 2020, with 375 observations. The data reached an all-time high of 20.700 % pa in Feb 1990 and a record low of 10.900 % pa in May 1999. Australia Lending Rate: Personal Loans: Unsecured Term Loans: Fixed data remains active status in CEIC and is reported by Reserve Bank of Australia. The data is categorized under Global Database’s Australia – Table AU.M004: Lending Rate.

  7. O

    SoE2020: Extent and rate of change of protected areas

    • data.qld.gov.au
    • researchdata.edu.au
    csv
    Updated Sep 25, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Environment, Tourism, Science and Innovation (2023). SoE2020: Extent and rate of change of protected areas [Dataset]. https://www.data.qld.gov.au/dataset/soe2020-extent-and-rate-of-change-of-protected-areas
    Explore at:
    csv(2764)Available download formats
    Dataset updated
    Sep 25, 2023
    Dataset authored and provided by
    Environment, Tourism, Science and Innovation
    License

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

    Description

    The protected area estate increased by more than 40,971 hectares between 1 January 2018 and 30 June 2020, and now covers about 8.24% of Queensland.

  8. r

    SoE2015: Extent and rate of change of protected areas

    • researchdata.edu.au
    • data.qld.gov.au
    Updated Sep 8, 2016
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    data.qld.gov.au (2016). SoE2015: Extent and rate of change of protected areas [Dataset]. https://researchdata.edu.au/soe2015-extent-rate-protected-areas/813536
    Explore at:
    Dataset updated
    Sep 8, 2016
    Dataset provided by
    data.qld.gov.au
    License

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

    Description

    The protected area estate increased by 3 million hectares between 2011-2015 and now covers 7.9% of Queensland.

  9. r

    Annual Growth Rate of Real GDP per capita

    • researchdata.edu.au
    • data.gov.au
    • +1more
    Updated Jul 2, 2018
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Sustainable Development Goals (2018). Annual Growth Rate of Real GDP per capita [Dataset]. https://researchdata.edu.au/annual-growth-rate-gdp-capita/2985817
    Explore at:
    Dataset updated
    Jul 2, 2018
    Dataset provided by
    data.gov.au
    Authors
    Sustainable Development Goals
    License

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

    Description

    Annual Growth Rate of Real GDP per capita

  10. T

    INFLATION RATE by Country in AUSTRALIA

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 26, 2017
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    INFLATION RATE by Country in AUSTRALIA [Dataset]. https://tradingeconomics.com/country-list/inflation-rate?continent=australia
    Explore at:
    excel, csv, json, xmlAvailable download formats
    Dataset updated
    May 26, 2017
    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
    2025
    Area covered
    Australia
    Description

    This dataset provides values for INFLATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

  11. A

    Australia AU: Inflation: GDP Deflator: Linked Series

    • ceicdata.com
    Updated Dec 28, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2022). Australia AU: Inflation: GDP Deflator: Linked Series [Dataset]. https://www.ceicdata.com/en/australia/inflation/au-inflation-gdp-deflator-linked-series
    Explore at:
    Dataset updated
    Dec 28, 2022
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Dec 1, 2011 - Dec 1, 2022
    Area covered
    Australia
    Variables measured
    Consumer Prices
    Description

    Australia Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data was reported at 7.115 % in 2022. This records an increase from the previous number of 2.801 % for 2021. Australia Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data is updated yearly, averaging 2.717 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 7.115 % in 2022 and a record low of -0.605 % in 2016. Australia Inflation:(GDP) Gross Domestic ProductDeflator: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Inflation. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years.;World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.;;

  12. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
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
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.

Search
Clear search
Close search
Google apps
Main menu