Sentiment Analysis for Effective Online Stock Market Prediction using RSS News Feeds and Stock Level Indicators

Abstract

In the field of stock market prediction, along with the historical prices of the stock market, RSS news feeds has great role in the forecast of financial exchange. Forecasting of the stock market is influenced by the RSS financial news feeds. This work is an endeavor to construct a system that identifies RSS news sentiments which may influence changes in stock information development patterns. The proposed approach analyses the sentiments in Really Simple Syndication (RSS) stock news feeds together with the historical prices of stock level indicators. Subsequently financial exchange RSS news feed information data is assembled for some time. Same way historical prices are also gathered for the same period of time. By combining both the results, the proposed method establishes an algorithm for sentiment analysis which can be used in the forecasting of stock market movement. In our exploratory investigation, the proposed technique finds that the RSS stock news feeds sentiment impact the forecast of stock value variances, regardless of whether up or down.