Performance of Sentiment Analysis Approaches to Predict the Stock Markets

Abstract

Stock markets are very important for every country and company to increase the economy of the country. Many researchers are trying to develop the accurate prediction of analysis of stock markets. Sentiment Analysis (SA) is the sub-domain in text mining. For the prediction of various opinions and text messages in several social networking sites (SNS). The role of sentiment analysis in stock markets plays a significant role in predicting accurate results in stock market analysis. Many companies are listed in the stock markets to increase the business and improve the stock prices of the specific company. For the past many years many people are investing in the stock markets. In this paper, the performance of various sentiment analyses in machine learning (ML) and Deep Learning (DL) approaches are discussed and analyzed the performance of the algorithms based on the prediction. The parameters such as Sensitivity, Specificity, Accuracy, F-measure and Area under curve (AUC).