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