100+ datasets found
  1. Forex News Annotated Dataset for Sentiment Analysis

    • zenodo.org
    • paperswithcode.com
    • +1more
    csv
    Updated Nov 11, 2023
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    Georgios Fatouros; Georgios Fatouros; Kalliopi Kouroumali; Kalliopi Kouroumali (2023). Forex News Annotated Dataset for Sentiment Analysis [Dataset]. http://doi.org/10.5281/zenodo.7976208
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    csvAvailable download formats
    Dataset updated
    Nov 11, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Georgios Fatouros; Georgios Fatouros; Kalliopi Kouroumali; 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
    
        <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p>
    
        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. C

    China CN: Heilongjiang: Chain: Fast Food: Purchase: Centralized Delivery:...

    • ceicdata.com
    Updated Dec 15, 2019
    + more versions
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    CEICdata.com (2019). China CN: Heilongjiang: Chain: Fast Food: Purchase: Centralized Delivery: Non-self Delivery Center [Dataset]. https://www.ceicdata.com/en/china/fast-food-heilongjiang/cn-heilongjiang-chain-fast-food-purchase-centralized-delivery-nonself-delivery-center
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    Dataset updated
    Dec 15, 2019
    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, 2008 - Dec 1, 2019
    Area covered
    China
    Variables measured
    Domestic Trade
    Description

    Heilongjiang: Chain: Fast Food: Purchase: Centralized Delivery: Non-self Delivery Center data was reported at 0.092 RMB bn in 2019. This records an increase from the previous number of 0.073 RMB bn for 2018. Heilongjiang: Chain: Fast Food: Purchase: Centralized Delivery: Non-self Delivery Center data is updated yearly, averaging 0.049 RMB bn from Dec 2008 (Median) to 2019, with 12 observations. The data reached an all-time high of 0.092 RMB bn in 2019 and a record low of 0.029 RMB bn in 2009. Heilongjiang: Chain: Fast Food: Purchase: Centralized Delivery: Non-self Delivery Center data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food: Heilongjiang.

  3. United States US: GDP: Growth: Gross Value Added: Services

    • ceicdata.com
    Updated Dec 15, 2010
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    CEICdata.com (2010). United States US: GDP: Growth: Gross Value Added: Services [Dataset]. https://www.ceicdata.com/en/united-states/gross-domestic-product-annual-growth-rate/us-gdp-growth-gross-value-added-services
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    Dataset updated
    Dec 15, 2010
    Dataset provided by
    CEIC Data
    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, 2004 - Dec 1, 2015
    Area covered
    United States
    Variables measured
    Gross Domestic Product
    Description

    United States US: GDP: Growth: Gross Value Added: Services data was reported at 2.621 % in 2015. This records an increase from the previous number of 2.221 % for 2014. United States US: GDP: Growth: Gross Value Added: Services data is updated yearly, averaging 2.335 % from Dec 1998 (Median) to 2015, with 18 observations. The data reached an all-time high of 4.456 % in 1999 and a record low of -1.772 % in 2009. United States US: GDP: Growth: Gross Value Added: Services data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Annual Growth Rate. Annual growth rate for value added in services based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Services correspond to ISIC divisions 50-99. They include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

  4. F

    High-Propensity Business Applications: Total for All NAICS in Texas

    • fred.stlouisfed.org
    json
    Updated Jun 11, 2025
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    (2025). High-Propensity Business Applications: Total for All NAICS in Texas [Dataset]. https://fred.stlouisfed.org/series/BAHBATOTALSATX
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    jsonAvailable download formats
    Dataset updated
    Jun 11, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Texas
    Description

    Graph and download economic data for High-Propensity Business Applications: Total for All NAICS in Texas (BAHBATOTALSATX) from Jul 2004 to May 2025 about high-propensity, business applications, business, TX, and USA.

  5. China CN: Chain: Fast Food: Purchase

    • ceicdata.com
    Updated Dec 15, 2024
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    CEICdata.com (2024). China CN: Chain: Fast Food: Purchase [Dataset]. https://www.ceicdata.com/en/china/fast-food/cn-chain-fast-food-purchase
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    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEIC Data
    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, 2012 - Dec 1, 2023
    Area covered
    China
    Variables measured
    Domestic Trade
    Description

    China Chain: Fast Food: Purchase data was reported at 48.630 RMB bn in 2023. This records an increase from the previous number of 38.302 RMB bn for 2022. China Chain: Fast Food: Purchase data is updated yearly, averaging 33.506 RMB bn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 48.630 RMB bn in 2023 and a record low of 6.928 RMB bn in 2005. China Chain: Fast Food: Purchase data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food.

  6. F

    High-Propensity Business Applications for Florida (DISCONTINUED)

    • fred.stlouisfed.org
    json
    Updated Jan 14, 2021
    + more versions
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    (2021). High-Propensity Business Applications for Florida (DISCONTINUED) [Dataset]. https://fred.stlouisfed.org/series/HPBUSAPPSAFL
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    jsonAvailable download formats
    Dataset updated
    Jan 14, 2021
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Florida
    Description

    Graph and download economic data for High-Propensity Business Applications for Florida (DISCONTINUED) (HPBUSAPPSAFL) from Q3 2004 to Q4 2020 about business applications, business, FL, and USA.

  7. T

    United States ADP Employment Change

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 2, 2025
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    TRADING ECONOMICS (2025). United States ADP Employment Change [Dataset]. https://tradingeconomics.com/united-states/adp-employment-change
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    csv, xml, json, excelAvailable download formats
    Dataset updated
    Jul 2, 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
    Feb 28, 2010 - Jun 30, 2025
    Area covered
    United States
    Description

    Private businesses in the United States fired -33 thousand workers in June of 2025 compared to 29 thousand in May of 2025. This dataset provides the latest reported value for - United States ADP Employment Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  8. China CN: Liaoning: Chain: Fast Food: Business Revenue: Meal & Commodity

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). China CN: Liaoning: Chain: Fast Food: Business Revenue: Meal & Commodity [Dataset]. https://www.ceicdata.com/en/china/fast-food-liaoning/cn-liaoning-chain-fast-food-business-revenue-meal--commodity
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    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, 2008 - Dec 1, 2019
    Area covered
    China
    Variables measured
    Domestic Trade
    Description

    Liaoning: Chain: Fast Food: Business Revenue: Meal & Commodity data was reported at 6.320 RMB bn in 2019. This records an increase from the previous number of 5.835 RMB bn for 2018. Liaoning: Chain: Fast Food: Business Revenue: Meal & Commodity data is updated yearly, averaging 5.269 RMB bn from Dec 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 6.320 RMB bn in 2019 and a record low of 0.710 RMB bn in 2005. Liaoning: Chain: Fast Food: Business Revenue: Meal & Commodity data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food: Liaoning.

  9. F

    High-Propensity Business Applications for Montana (DISCONTINUED)

    • fred.stlouisfed.org
    json
    Updated Jan 14, 2021
    + more versions
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    (2021). High-Propensity Business Applications for Montana (DISCONTINUED) [Dataset]. https://fred.stlouisfed.org/series/HPBUSAPPSAMT
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jan 14, 2021
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for High-Propensity Business Applications for Montana (DISCONTINUED) (HPBUSAPPSAMT) from Q3 2004 to Q4 2020 about business applications, MT, business, and USA.

  10. C

    China CN: Liaoning: Chain: Fast Food: Purchase: Centralized Delivery: Self...

    • ceicdata.com
    Updated Sep 15, 2020
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    CEICdata.com (2020). China CN: Liaoning: Chain: Fast Food: Purchase: Centralized Delivery: Self Delivery Center [Dataset]. https://www.ceicdata.com/en/china/fast-food-liaoning/cn-liaoning-chain-fast-food-purchase-centralized-delivery-self-delivery-center
    Explore at:
    Dataset updated
    Sep 15, 2020
    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, 2008 - Dec 1, 2019
    Area covered
    China
    Variables measured
    Domestic Trade
    Description

    Liaoning: Chain: Fast Food: Purchase: Centralized Delivery: Self Delivery Center data was reported at 4.934 RMB bn in 2019. This records an increase from the previous number of 4.563 RMB bn for 2018. Liaoning: Chain: Fast Food: Purchase: Centralized Delivery: Self Delivery Center data is updated yearly, averaging 0.205 RMB bn from Dec 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 4.934 RMB bn in 2019 and a record low of 0.042 RMB bn in 2009. Liaoning: Chain: Fast Food: Purchase: Centralized Delivery: Self Delivery Center data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food: Liaoning.

  11. F

    High-Propensity Business Applications: Administrative and Support in the...

    • fred.stlouisfed.org
    json
    Updated Jun 11, 2025
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    (2025). High-Propensity Business Applications: Administrative and Support in the United States [Dataset]. https://fred.stlouisfed.org/series/BAHBANAICS56SAUS
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    jsonAvailable download formats
    Dataset updated
    Jun 11, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Graph and download economic data for High-Propensity Business Applications: Administrative and Support in the United States (BAHBANAICS56SAUS) from Jul 2004 to May 2025 about high-propensity, administrative, business applications, business, and USA.

  12. United States SBOI: sa: Most Pressing Problem: Survey High: Others

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). United States SBOI: sa: Most Pressing Problem: Survey High: Others [Dataset]. https://www.ceicdata.com/en/united-states/nfib-index-of-small-business-optimism/sboi-sa-most-pressing-problem-survey-high-others
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    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, 2024 - Feb 1, 2025
    Area covered
    United States
    Variables measured
    Business Confidence Survey
    Description

    United States SBOI: sa: Most Pressing Problem: Survey High: Others data was reported at 31.000 % in Mar 2025. This stayed constant from the previous number of 31.000 % for Feb 2025. United States SBOI: sa: Most Pressing Problem: Survey High: Others data is updated monthly, averaging 31.000 % from Jan 2014 (Median) to Mar 2025, with 131 observations. The data reached an all-time high of 31.000 % in Mar 2025 and a record low of 31.000 % in Mar 2025. United States SBOI: sa: Most Pressing Problem: Survey High: Others data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s United States – Table US.S042: NFIB Index of Small Business Optimism. [COVID-19-IMPACT]

  13. F

    High-Propensity Business Applications for California (DISCONTINUED)

    • fred.stlouisfed.org
    json
    Updated Jan 14, 2021
    + more versions
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    (2021). High-Propensity Business Applications for California (DISCONTINUED) [Dataset]. https://fred.stlouisfed.org/series/HPBUSAPPSACA
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jan 14, 2021
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    California
    Description

    Graph and download economic data for High-Propensity Business Applications for California (DISCONTINUED) (HPBUSAPPSACA) from Q3 2004 to Q4 2020 about business applications, business, CA, and USA.

  14. F

    High-Propensity Business Applications for District of Columbia

    • fred.stlouisfed.org
    json
    Updated Jul 10, 2025
    + more versions
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    (2025). High-Propensity Business Applications for District of Columbia [Dataset]. https://fred.stlouisfed.org/series/HBUSAPPWNSADCYY
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    jsonAvailable download formats
    Dataset updated
    Jul 10, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Washington
    Description

    Graph and download economic data for High-Propensity Business Applications for District of Columbia (HBUSAPPWNSADCYY) from 2007-01-06 to 2025-06-28 about business applications, DC, business, and USA.

  15. China CN: Guangdong: Chain: Fast Food: Business Revenue: Meal & Commodity

    • ceicdata.com
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    CEICdata.com, China CN: Guangdong: Chain: Fast Food: Business Revenue: Meal & Commodity [Dataset]. https://www.ceicdata.com/en/china/fast-food-guangdong/cn-guangdong-chain-fast-food-business-revenue-meal--commodity
    Explore at:
    Dataset provided by
    CEIC Data
    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, 2008 - Dec 1, 2019
    Area covered
    China
    Variables measured
    Domestic Trade
    Description

    Guangdong: Chain: Fast Food: Business Revenue: Meal & Commodity data was reported at 22.723 RMB bn in 2019. This records an increase from the previous number of 21.303 RMB bn for 2018. Guangdong: Chain: Fast Food: Business Revenue: Meal & Commodity data is updated yearly, averaging 16.252 RMB bn from Dec 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 22.723 RMB bn in 2019 and a record low of 2.821 RMB bn in 2005. Guangdong: Chain: Fast Food: Business Revenue: Meal & Commodity data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food: Guangdong.

  16. F

    High-Propensity Business Applications: Total for All NAICS in Florida

    • fred.stlouisfed.org
    json
    Updated Jul 10, 2025
    + more versions
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    (2025). High-Propensity Business Applications: Total for All NAICS in Florida [Dataset]. https://fred.stlouisfed.org/series/BAHBATOTALNSAFL
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 10, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Florida
    Description

    Graph and download economic data for High-Propensity Business Applications: Total for All NAICS in Florida (BAHBATOTALNSAFL) from Jul 2004 to Jun 2025 about high-propensity, business applications, business, FL, and USA.

  17. F

    High-Propensity Business Applications: Total for All NAICS in Illinois

    • fred.stlouisfed.org
    json
    Updated Jul 10, 2025
    + more versions
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    (2025). High-Propensity Business Applications: Total for All NAICS in Illinois [Dataset]. https://fred.stlouisfed.org/series/BAHBATOTALNSAIL
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 10, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Illinois
    Description

    Graph and download economic data for High-Propensity Business Applications: Total for All NAICS in Illinois (BAHBATOTALNSAIL) from Jul 2004 to Jun 2025 about high-propensity, business applications, IL, business, and USA.

  18. China CN: Chain: Fast Food: Purchase: Centralized Delivery

    • ceicdata.com
    Updated Nov 13, 2024
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    CEICdata.com (2024). China CN: Chain: Fast Food: Purchase: Centralized Delivery [Dataset]. https://www.ceicdata.com/en/china/fast-food/cn-chain-fast-food-purchase-centralized-delivery
    Explore at:
    Dataset updated
    Nov 13, 2024
    Dataset provided by
    CEIC Data
    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, 2012 - Dec 1, 2023
    Area covered
    China
    Variables measured
    Domestic Trade
    Description

    China Chain: Fast Food: Purchase: Centralized Delivery data was reported at 36.390 RMB bn in 2023. This records an increase from the previous number of 29.355 RMB bn for 2022. China Chain: Fast Food: Purchase: Centralized Delivery data is updated yearly, averaging 28.100 RMB bn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 36.769 RMB bn in 2019 and a record low of 6.319 RMB bn in 2005. China Chain: Fast Food: Purchase: Centralized Delivery data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food.

  19. F

    High-Propensity Business Applications: Total for All NAICS in Delaware

    • fred.stlouisfed.org
    json
    Updated Jul 10, 2025
    + more versions
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    (2025). High-Propensity Business Applications: Total for All NAICS in Delaware [Dataset]. https://fred.stlouisfed.org/series/BAHBATOTALNSADE
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 10, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for High-Propensity Business Applications: Total for All NAICS in Delaware (BAHBATOTALNSADE) from Jul 2004 to Jun 2025 about high-propensity, business applications, DE, business, and USA.

  20. F

    All Employees: Professional and Business Services in Rapid City, SD (MSA)

    • fred.stlouisfed.org
    json
    Updated Mar 18, 2025
    + more versions
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    (2025). All Employees: Professional and Business Services in Rapid City, SD (MSA) [Dataset]. https://fred.stlouisfed.org/series/SMU46396606000000001A
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    jsonAvailable download formats
    Dataset updated
    Mar 18, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Rapid City, South Dakota
    Description

    Graph and download economic data for All Employees: Professional and Business Services in Rapid City, SD (MSA) (SMU46396606000000001A) from 1990 to 2024 about Rapid City, professional, SD, business, services, employment, and USA.

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Georgios Fatouros; Georgios Fatouros; Kalliopi Kouroumali; Kalliopi Kouroumali (2023). Forex News Annotated Dataset for Sentiment Analysis [Dataset]. http://doi.org/10.5281/zenodo.7976208
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Forex News Annotated Dataset for Sentiment Analysis

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Dataset updated
Nov 11, 2023
Dataset provided by
Zenodohttp://zenodo.org/
Authors
Georgios Fatouros; Georgios Fatouros; Kalliopi Kouroumali; 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

    <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p>

    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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