#CurrencyPairPrediction Sentiment Scoring Three main ways: 1. Lexicon-Based Methods Use dictionaries where words are labeled: • Positive words: “bullish”, “buy”, “gain”, “strong” • Negative words: “bearish”, “sell”, “loss”, “weak” Examples: • VADER (Valence Aware Dictionary for Sentiment Reasoning): Specially tuned for social media! • TextBlob (easy and quick) VADER example in Python: from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer analyzer = SentimentIntensityAnalyzer() score = analyzer.polarity_scores(“Fed gonna pump USD, stay long!”) print(score) Output: {‘neg’: 0.0, ‘neu’: 0.464, ‘pos’: 0.536, ‘compound’: 0.7717} → Positive sentiment detected!