Analysis of stock market using text mining and natural language processing

Stock market has become one of the major components of economy not only in developed countries but also in third world developing countries. Making decision in stock market is not really easy because a lot of factors are involved with every choice we make. Therefore, a lot of analysis is required to make an optimal move on stock market which may involve price trend, market´s nature, company´s stability, different news and rumors about stocks etc. The objective of this study is to extract fundamental information from relevant news sources and use them to analyze or sometimes forecast the stock market from the common investor´s viewpoint. We surveyed the existing business text mining researches and proposed a framework that uses our text parser and analyzer algorithm with an open source natural language processing tool to analyze (machine learning and text mining), retrieve (natural language processing), forecast (compare with historic data) investment decisions from any text data source on stock market. For our research we used the data of Dhaka Stock Exchange (DSE), capital market of Bangladesh.