Nowadays, most of information saved in companies are unstructured models. Retrieval and extraction of the information is essential works and importance in semantic web areas. Many of these requirements will be depend on the unstructured data analysis. More than 80% of all potentially useful business information is unstructured data, in kind of sensor readings, console logs and so on. The large number and complexity of unstructured data opens up many new possibilities for the analyst. Text mining and natural language processing are two techniques with their methods for knowledge discovery from textual context in documents. This is an approach to organize a complex unstructured data and to retrieve necessary information. The paper is to find an efficient way of storing unstructured data and appropriate approach of fetching data. Unstructured data targeted in this work to organize, is the public tweets of Twitter. Building an Big Data application that gets stream of public tweets from twitter which is latter stored in the HBase using Hadoop cluster and followed by data analysis for data retrieved from HBase by REST calls is the pragmatic approach of this project.