Prediction of stock market using artificial intelligence and sentiment analysis - Lee, Shanice Shi Ying (Nanyang Technological University, 2024)
This paper delves into enhancing stock price prediction using Artificial Intelligence (AI) and Machine Learning (ML) techniques, given the stock market's unpredictable and dynamic nature. Three ML models, namely Decision Tree (DT), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), were employed to predict Tesla's stock prices and its trends. Sentiment Analysis using TextBlob model was also integrated to leverage its accuracy in analyzing sentiments from textual data using Elon Musk's tweets. By incorporating Sentiment Analysis into the LSTM model, nuanced market sentiments can be captured which improves the model's ability to detect sentiment-driven trends in stock prices. The predictive accuracy of the models was assessed using performance metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2).