Deep Belief Network and Sentimental analysis for extracting on multi-variable Features to predict Stock market Performance and accuracy

Deep Learning shows a drastic growth in many fields such as medical, voice recognitions, Siri Alexa and computer vision, so on. Even though machine learning and deep learning are developing point in data science the back bone for all these platforms are Big data Analytics. A massive data and information's from all the website, social media and other networks produced so called Big data are focused in day to day life. When these information are collected from the various chat history such as Whatsapp, Facebook, Twitter and other for generating numerous development such as privacy policy, investing, stock markets, business, study process and many more. Professional involvement deals the deep learning concept to focus on the stock market procedure in particular to develop the Business enterprise, individual profits, product strategies and other decision making process also. However the main gap to be filled in this prediction was to look around the internet sources as well as real time population for stock market varies its accuracy due to the lack of hidden layer interaction. Here we propose a deep learning accuracy prediction named as sentimental analysis to perform an accuracy in a best way by applying Bi-directional long-short term memory (Bi-LSTM) and Deep belief network to overcome the issues and less accuracy given by doc2vec, longshort term memory (LSTM) and provides a good model for our sentimental Bi-LSTM model to find the best stock market analysis.