Utilizing X Sentiment Analysis to Improve Stock Price Prediction Using Bidirectional Long Short-Term Memory

Abstract

The capital market is one of the important factors that influence the national economy. However, the stock price in capital market fluctuates over time. Therefore, the investors strongly need an accurate prediction of stock price for making profitable decision. However, with the pervasive influence of the internet, investors and investment institutions have started incorporating online opinions and news, including those found on social media platforms like X. This research aims to enhance stock price prediction by utilizing X sentiment analysis. The sentiment of tweets from X related to IHSG stock price is predicted by using BERT (Bidirectional Encoder Representations from Transformers), then its result isintegrated with the historical stock price data for predicting future stock price by using BiLSTM (Bidirectional Long Short-Term Memory). The experiment results show that the RMSE and MAPE of the proposed model with sentiment analysis is decreased by 0.042 and 0.595, resepectively, compared to the model without sentiment analysis. Therefore, it can be concluded that the inclusion of X sentiment analysis in conjunction with BiLSTM succeeded in improving the performance of stock price prediction. The study's outcome is expected to be valuable for investors to make profitable decisions, leveraging the information available on social media.