The share market is the new business for many individuals and companies.The stock market attracts many as there is a huge profitinvolved. But there can be huge loss as well. The world suffered from many economic crises due to itsdownfall, which may have cost lives and way of living of many. If the share prices couldbe predicted, this can help in avoiding the economic crisis.Many business analysts said that the share market cannot be predicted and it is completelyrandom, but there is no common opinion about that. Many also said that it can bepredicted but with using different measures. In this paper, we have proposed a technique to perform stock market data analysis to find if there exists a relation between stock price change of two companies- TCS and Infosys. This is done using Big data technique by performing sentiment analysis of Tweets from Twitter and finding the correlation. Also, machine learning techniques are applied on the BSE data of the companies to predict the stock prices of next day. The results of allthe factors like sentiments, similar industrial shares, and index shares are combined to get to the conclusion. We found that the share market depends on the numerous factors and considering only certain factorsare insufficient for analysis.

Hadoop, Map-reduce, Machine learning, Sentiment analysis, Stock Market

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