Real-Time Stock Trend Prediction via Sentiment Analysis of News Article

Abstract

The stock market is volatile and volatility occurs in clusters, price fluctuations based on sentiment and news reports are common. A trader uses a wide variety of publicly available information to forecast the marketing decision. This paper proposes an advice to traders for stock trading using sentimental analysis of publically available news reports. It is based on a hypothesis, that news articles have an impact on the stock market, with this hypothesis we study the relationship between news and stock trend and also proved that negative news has a persistent effect on the stock market. In order to prove this assumption semi-supervised learning technique is being used to build the final model of news classification. This research shows that SVM with TF-IDF as feature performs well in further analysis. The accuracy of the prediction model is more than 90% having 52% correlation with the return label of a stock. This paper also proposes a real-time system which fetches news of any company on a real-time basis and displays its top five news and also predicts the adjusted close price of the next seven days.

Keywords: Text Mining, Human Sentiments, KNN, Random Forest, Multinomial Naïve Bayes, linear SVM, News.