The tools used in the digital project I chose were used for gathering past financial sentiment. They gathered public data from old financial news articles that were published on the web and paired them with past stock price data that matched the time frame of the news articles. This tool they created uses two forms os digital tools to create a tool with more real-world application.
Taking digitized news articles and digitized stock ticket prices to combine into a way to predict stock price movement based on the sentiment in the news.
In the publication I chose the authors who use Natural Language Processing. This digital tool analyzes words in texts and finds their dictionary-based sentiment. The researchers analyzed more than 4.5 million news articles published between 1991 and 1025. They used digital methods to (NLP) to find positive and negative words within the articles and create a sentiment score based of the language used in them. This sentiment score gives them a representation of the overall tone of the news. They used these sentiments cores to create daily news sentiment indexes across 25 countries and compare the changes in sentiment with international stock market prices and returns. This makes the publication an example of digital humanities by using computational analysis to study collections of human created texts that would be nearly impossible and definitely impractical to analyze manually.