Performance Evaluation of Deep Learning and Machine Learning Techniques for Opinion Mining

Abstract

With the technological developments in the fields of natural language processing (NLP) and opinion mining (OM), many real-time applications are concentrating on analyzing the opinions of the people. The opinions or reviews given by the people through the internet are collected for summarization or classification based on the need. The feature selection typically saves the operating time, eliminates irrelevant features and redundancy. For feature selection, a semantic based feature selection algorithm called information gain (IG) is used. Naive Bayes, bagging, support vector machines (SVM), classification and regression trees (CART), and algorithms along with optimization techniques like ant colony optimization algorithms are used to optimize and classify the opinions. Also, in this chapter, the state-of-the art machine learning technique, deep learning, is also involved with the convolution neural networks (CNN) algorithm to identify the positive and negative opinions in different fields such as movie reviews, emojis and medical data.