DOI: 10.5281/zenodo.7976208
title: Forex News Annotated Dataset for Sentiment Analysis

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.
description: If you use this in your research, please cite the following research article: 

Fatouros, G., Soldatos, J., Kouroumali, K., Makridis, G., & Kyriazis, D. (2023). Transforming sentiment analysis in the financial domain with chatgpt. Machine Learning with Applications, 100508.
 @article{fatouros2023transforming,
  title={Transforming sentiment analysis in the financial domain with chatgpt},
  author={Fatouros, Georgios and Soldatos, John and Kouroumali, Kalliopi and Makridis, Georgios and Kyriazis, Dimosthenis},
  journal={Machine Learning with Applications},
  pages={100508},
  year={2023},
  publisher={Elsevier}
}