Global stock markets reflect key shifts in national economies, attracting an excess of investors. The market movements are based on past market changes. Predicting the direction of the stock market is the most crucial factor influencing financial investors’ decisions to buy, hold or sell shares. Thus, investors are always worried about analysing and projecting long-term stock market patterns to maximise earnings. In this paper, various models have been used to potentially replicate the behaviour of the stock market, and a comparison has been made. The models included are Statistical Analysis, Regression Analysis, SVM and LSTM. This paper also examines the effect of Twitter sentiment on the stock market. The proposed approach is assessed using RELIANCE and RCOM share price data. The experimental results illustrate that the proposed approach using LSTM shows better results in comparison to the state-of-the-art method.