A Review of Text Analytics Using Machine Learning

Abstract

In recent years, data analysis has been widely applied in many different domains. Text data plays an important role in prediction of various insights. The data produced in the form of user reviews, satisfactory forms, movie reviews, after sales product reviews, and similar kinds of representations serve as inputs for textual data analysis. In previous years, however, companies relied on paper-based satisfactory surveys, agent reports, etc. for business development or product outreach development purposes. As these methods involve human intervention, there is always a very high chance of false inputs. Hence, the development of computational intelligence-based strategies such as textual and sentimental analysis have been of enormous help for such companies. Automated tools and software have helped various business organizations and firms to develop their business, find their faults and bugs, and relieve themselves from their limitations. This chapter discusses the basics of textual analysis, approaches for textual analysis, as well as the tools, solutions, and some limitations of applying textual analysis.