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In the recent, past accurate forecasting of stock market has become a challenging task. The proposed approach helps to achieve high precision in stock market prediction by combining the sensex points, with Really Simple Syndication (RSS) news feeds and Tweets. The algorithm focuses on the correlation between the stock market values, sentiments of tweets and RSS feeds for a particular period of time. In this algorithm, a trained model is used for stock market prediction rates. Experimental study focuses on the stock market sensex prices, RSS news feeds and tweets which are collected for the company ARBK from Amman Stock Exchange (ASE). This paper follows two types of hypothesises. Null Hypothesis H0: Stock level indicators predict the trend of stock rates at an allowable rate of minimum 80% above. Alternate Hypothesis Ha: Stock level indicators along with the sentiment analysis of RSS news feeds and tweets as stock enhances the accuracy of prediction. Our experimental study has proved the correlation between Stock level indicators and RSS news feeds and tweets and has shown significant improvement of 20% in prediction accuracy.