#AITradingAffectsForex AI-driven classification of Forex news articles involves using machine learning and natural language processing (NLP) techniques to automatically categorize news based on sentiment, topic, or impact on currency markets. These systems analyze textual data to identify trends, predict market movements, and assist traders in decision-making. Key components include: Data Preprocessing: Tokenization, stop-word removal, and sentiment tagging. Machine Learning Models: Algorithms like Naïve Bayes, Support Vector Machines (SVM), and deep learning models such as transformers (e.g., BERT). Sentiment Analysis: Identifying positive, negative, or neutral tones in news. Event Detection: Recognizing key economic events affecting Forex markets. Real-time Processing: Analyzing and classifying articles in real-time for timely decision-making. Such AI-driven classification enhances trading strategies by filtering relevant information, reducing noise, and improving response speed to market changes.