Much is being said about the use of artificial intelligence to assist in the financial markets but there is a surprising lack of actual data supporting one trading algorithm over another. While there are numerous research that explore the possibilities of machine learning and deep learning techniques for stock market prediction, due to the lack of inter-domain expertise (people who are experts in both data science and finance), most of these projects use relatively elementary methods and fail to account for many underlying constraints. This paper aims to create a stock market investment aide which predicts the price action of stock market instruments using neural networks that learn time series patterns in the historical price data, correlates the prediction with the sentiment score of the market for that day and thereby give the investor/trader a buy or sell signal. The model developed has been successful in predicting price action of the NIFTY50 index with an accuracy of 89 percent. The prediction aided with the correlation from the sentiment analysis can give the investor an added confidence while making investment decisions on the stock market.