An Information Processing Model for Assessment of User Reviews

Abstract

This Analysis of reviews became a valuable source of accurate information. In this research, we will analyze reviews. Analyzed reviews will create an accurate data set. Fitting an appropriate model is necessary to get accuracy. Problems in fitting an appropriate model are under-fitting and over-fitting. An under-fit model will be less flexible and cannot account for the data. Over-fitting is a modeling error that occurs when a function is too closely fit to a limited set of data points. To solve the mentioned problems, features and appropriate algorithms are selected. As a solution, we will perform preprocessing in a better way with the machine learning algorithms to assess the impact of preprocessing. The main focus of this research is to assess the impact of preprocessing steps on different classifiers.