Financial NLP: Sentiment, Events, and Market Impact

Module: Fintech AI | Difficulty: Advanced

Financial Sentiment

Event Detection

Market Reaction

Data Sources

| Source | Latency | Coverage | |--------|---------|----------| | News | Minutes | Broad | | Social | Real-time | Narrow | | Filings | Hours | Narrow | | Earnings calls | Real-time | Narrow |

import numpy as np
from textblob import TextBlob

class FinancialSentiment:
    def __init__(self):
        self.dict = self.load_sentiment_dict()
    def analyze(self, text):
        # Financial-specific sentiment
        words = text.lower().split()
        pos = sum(1 for w in words if w in self.dict['positive'])
        neg = sum(1 for w in words if w in self.dict['negative'])
        return (pos - neg) / max(len(words), 1)
    def event_impact(self, event_text, stock_returns):
        sentiment = self.analyze(event_text)
        # Correlate with returns
        return sentiment * stock_returns

Research Insight: Financial NLP requires domain-specific models because general sentiment models misinterpret financial language (e.g., "volatile" is negative in general but neutral in finance). Fine-tuning on financial text improves accuracy by 15-20%.