#AIImpactOnForex Hybrid approaches to Forex sentiment analysis combine the strengths of both lexicon-based and machine learning techniques. These methods often use lexicon scores as features within machine learning models, leveraging the domain-specific knowledge embedded in the lexicons while allowing the models to learn more complex patterns and contextual nuances from training data. For instance, a hybrid system might use a financial lexicon to initialize sentiment scores and then train a machine learning classifier on a corpus of Forex news and social media data, using these initial scores along with other linguistic features. This can lead to more robust and accurate sentiment predictions compared to relying solely on either approach. The goal is to harness the interpretability of lexicon methods and the adaptability of machine learning.