Big Data has become a very important part of all industries and organizations sectors nowadays. All sectors like
energy, banking, retail, hardware, networking, etc all generate a huge amount of unstructured data which is
processed and analyzed accurately in a structured form. Then the structured data can reveal very useful
information for their business growth. Big Data helps in getting useful data from unstructured or heterogeneous
data by analyzing them. Big data initially defined by the volume of a data set. Big data sets are generally huge,
measuring tens of terabytes and sometimes crossing the sting of petabytes. Today, big data falls under three
categories structured, unstructured, and semi-structured.
The size of big data is improving in a fast phase from Terabytes to Exabytes Of data. Also, Big data requires
techniques that help to integrate a huge amount of heterogeneous data and to process them. Data Analysis which is
a big data process has its applications in various areas such as business processing, disease prevention,
cybersecurity, and so on. Big data has three major issues such as data storage, data management, and information
retrieval. Big data processing requires a particular setup of hardware and virtual machines to derive results. The
processing is completed simultaneously to realize results as quickly as possible. These days big data processing
techniques include Text mining and sentimental analysis.
Text analytics is a very large field under which there are several techniques, models, methods for automatic and
quantitative analysis of textual data. The purpose of this paper is to show how the text analysis and sentimental
analysis process the unstructured data and how these techniques extract meaningful information and, thus make
information available to the various data mining statistical and machine learning) algorithms.
Research on Unstructured Data Processing Technology in Executing Audit Based on Big Data Budget