Product Sentiment Assessment using Large Scale Cloud System

Abstract

A typical manner in which valuable information can be obtained by means of extracting the sentiment or also the opinion from any message is called sentiment analysis. The sentiment classification exploits the technologies in machine learning owing to their ability to learn from training data set to predict and support decision making with high accuracy level. Some algorithms do not maintain proper scalability for large datasets. Today, there are several disciplines that have the need to deal with some big datasets for involving features in high numbers. The methods of feature selection have been aiming at the elimination of the noisy, the irrelevant or the redundant features that can bring down the performance of classification. Most of the traditional methods lack the scalability to be able to cope with the results within a given time. Here in this work, Term Frequency (TF) that is a method of feature extraction has been used. The focus has been on the selection for the opinion mining by using the Information Gain (IG) based method and compared with the method of. All these methods of feature selection have reduced all the original feature sets by means of removing the features that are irrelevant to enhance the accuracy of classification and bring down the running time of the learning algorithms. The method proposed has been evaluated by means of using the Support Vector Machine (SVM) based classifier. The experimental results have proved that the proposed method had achieved better performance.