This paper explores the relationship between sentiment expressed in tweets about advancements in generative artificial intelligence (AI) and day-to-day fluctuations in stock prices of associated companies. The study focuses on tweets containing hashtags related to ChatGPT from December 2022 to March 2023, using natural language processing to extract sentiment scores. Machine learning models, including gradient boosting, decision trees, and random forests, are employed to predict stock price movements for key companies like Microsoft and OpenAI. The research integrates sentiment analysis with macroeconomic indicators, the Twitter uncertainty index, and stock market data to forecast bullish or bearish trends. Preliminary findings suggest a plausible correlation between public sentiment in Twitter discussions about AI and market valuation and trading activities. The study aims to enhance the accuracy of stock market predictions by leveraging sentiment analysis from a large dataset of 500,000 tweets, complemented by financial data and control variables. The results indicate that sentiment analysis, when combined with macroeconomic and company-specific data, can significantly improve the predictive capabilities of stock market models.This paper explores the relationship between sentiment expressed in tweets about advancements in generative artificial intelligence (AI) and day-to-day fluctuations in stock prices of associated companies. The study focuses on tweets containing hashtags related to ChatGPT from December 2022 to March 2023, using natural language processing to extract sentiment scores. Machine learning models, including gradient boosting, decision trees, and random forests, are employed to predict stock price movements for key companies like Microsoft and OpenAI. The research integrates sentiment analysis with macroeconomic indicators, the Twitter uncertainty index, and stock market data to forecast bullish or bearish trends. Preliminary findings suggest a plausible correlation between public sentiment in Twitter discussions about AI and market valuation and trading activities. The study aims to enhance the accuracy of stock market predictions by leveraging sentiment analysis from a large dataset of 500,000 tweets, complemented by financial data and control variables. The results indicate that sentiment analysis, when combined with macroeconomic and company-specific data, can significantly improve the predictive capabilities of stock market models.