Jinmo Yan¹ and Garret Washburn², ¹University of Pennsylvania, USA, ²California State Polytechnic University, USA

This paper presents an intelligent system that combines social media sentiment analysis with historical stock data to predict stock price movements [1]. Built as a mobile application using Flutter, the system integrates a Flask web server and a fine-tuned OpenAI model to process social sentiment and make real-time stock predictions [2]. Through experiments, we tested the system's ability to correlate sentiment with stock price fluctuations and volatility. The results showed that while sentiment analysis enhances prediction accuracy, it also introduces volatility. Key challenges include handling noisy social media data and over-reliance on sentiment. Future improvements involve refining the sentiment analysis model and incorporating additional market factors [3]. This system provides investors with a more dynamic tool to assess market trends based on real-time sentiment.

Stock Prediction, Social Media Sentiment, AI Fine-Tuning, Flutter Mobile Application, Market Volatility