Recently some efforts have been made to use data from social media for prediction in various domains. Some studies also suggest public emotions in tweets may correlate with market trend. Using studies observations, public sentiments from social media helps to predict stock price of a particular company. The system can be effectively used to predict stock price movement of particular company or not. However, the current systems use to predict the overall market trend instead of predicting for individual company. Also, the public sentiment is not only the single factor which can affect the stock market so, there is need of combination of market and public sentiment to predict the stock price of the company. The system firstly mine the tweets of a particular company from different sources such as Twitter and Yahoo finance and news related to finance are also considered. The data is preprocessed to counter the noisy and missing data and sentiment of the public data of company is calculated using the natural language processing techniques. Financial data values of company will be fetched from yahoo finance. Some classifiers techniques such as SVM, Naïve Bayes and Maximum Entropy are also tested to find the best outcome. The public sentiment and market are sentiments are combined to obtain the desired outcome. The correlation between public’s sentiment and company’s stock price movements is observed. Granger causality test is used to determine whether sentiment polarity is able to predict the stock price in advance for a company. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.