#AITradingAffectsForex Machine Learning for Sentiment Analysis in Currency Markets Sentiment analysis in currency markets involves using machine learning (ML) to analyze textual data, such as news, social media, and financial reports, to predict currency price movements. Key aspects include: 1. Data Sources – ML models extract insights from financial news, social media (e.g., Twitter), central bank statements, and analyst reports. 2. Text Processing – Natural Language Processing (NLP) techniques like tokenization, word embeddings (Word2Vec, BERT), and sentiment lexicons classify text as positive, negative, or neutral. 3. ML Models – Supervised models (logistic regression, SVM, random forests) and deep learning (LSTMs, transformers) are used for sentiment classification and price prediction. 4. Feature Engineering – Market sentiment indicators, volatility measures, and event-driven sentiment signals are created for predictive modeling. 5. Impact on Forex Markets – Positive sentiment can lead to currency appreciation, while negative sentiment can cause depreciation, influencing trading strategies. 6. Challenges – Noisy data, sarcasm detection, context understanding, and real-time analysis complexity remain challenges in effective sentiment-based trading models. ML-powered sentiment analysis enhances forex trading by providing data-driven insights, improving risk management, and automating trading strategies.