#AITradingAffectsForex Sentiment Analysis in AI Trading Sentiment analysis in AI trading involves using natural language processing (NLP) and machine learning to assess market sentiment based on news articles, social media posts, financial reports, and other textual data. The AI trading bot then makes buy or sell decisions based on this sentiment. ⸻ How Sentiment Analysis Works in AI Trading 1. Data Collection • The AI bot gathers text data from various sources: • Financial news websites (Bloomberg, CNBC, Reuters) • Social media (Twitter, Reddit, StockTwits) • Analyst reports and earnings call transcripts • Regulatory filings and economic reports 2. Text Processing & NLP • The bot cleans the text and processes it to extract meaning using NLP techniques: • Tokenization: Breaking text into words or phrases • Stopword Removal: Removing common words (e.g., “the”, “is”) • Stemming/Lemmatization: Reducing words to their root form (e.g., “buying” → “buy”) • Named Entity Recognition (NER): Identifying key entities (companies, executives, events) 3. Sentiment Scoring • The AI assigns a sentiment score (positive, neutral, or negative) based on: • Lexicon-based methods: Matching words with a predefined dictionary of sentiment scores • Machine learning models: Training AI to recognize positive or negative sentiment • Deep learning (LSTMs, transformers like GPT): Understanding complex language nuances 4. Market Impact Prediction • The AI bot correlates sentiment scores with historical price movements.