Gold Price Prediction using Sentiment Analysis

Abstract

Gold is one of the valuable materials that is used for funding trading purchases. Nowadays, more investors are interested in gold investments due to the sudden increase in gold prices. However, transactions involving gold are risky, the price of gold fluctuates wildly due to the unpredictability of the gold market. Hence, there is a need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper analyzes the correlation between gold price movements and sentiments of Arabic tweets in Egypt. After performing sentiment analysis on these tweets, three supervised machine learning algorithms were used for predicting the gold price. The algorithms include Multiple linear regression, Ridge regression, and Lasso regression. The result of this work shows that the Lasso regression model performs better than the other two models. However, it is concluded that there is a weak correlation between gold prices and Twitter data. Therefore, gold prices cannot be accurately predicted using Twitter data alone.