  The objective of this research is to examine how sentiment analysis can be
employed to generate trading signals for the Foreign Exchange (Forex) market.
The author assessed sentiment in social media posts and news articles
pertaining to the United States Dollar (USD) using a combination of methods:
lexicon-based analysis and the Naive Bayes machine learning algorithm. The
findings indicate that sentiment analysis proves valuable in forecasting market
movements and devising trading signals. Notably, its effectiveness is
consistent across different market conditions. The author concludes that by
analyzing sentiment expressed in news and social media, traders can glean
insights into prevailing market sentiments towards the USD and other pertinent
countries, thereby aiding trading decision-making. This study underscores the
importance of weaving sentiment analysis into trading strategies as a pivotal
tool for predicting market dynamics.
