Author : Rajvir Kaur 1
Date of Publication :31st December 2021
Abstract: Because of its unpredictable and non-linear character, accurate stock market prediction is a difficult and time-consuming undertaking for researchers. Several studies are based on previous data to determine the future worth of the stock market. However, external variables such as social media and news headlines now have a significant impact on the stock market. This study is based on predicting future stock prices using Twitter social media and news data, as well as historical data, to achieve high prediction outcomes. Machine learning techniques are employed in this article to forecast future stock values of five blue-chip corporations from various sectors. To construct the final dataset, the data was collected from many sources, including Twitter, news, and historical data, and then examined. Then, using the final dataset, we deployed three machine learning algorithms – SVM, Logistic regression, and Random forest – to forecast the model's accuracy and compared the algorithms to determine the best algorithm for stock market prediction. The results demonstrate that logistic regression earned the highest prediction accuracy for five blue-chip businesses, ranging from 85 percent to 89 percent, and a comparative analysis shows that logistic regression performed better than SVM and Random forest algorithms
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