Support Vector Machine based Word Embedding and Feature Reduction for Sentiment Analysis-A Study

Abstract

Sentiment analysis (SA), also called as opinion mining is the technique used to bring together the opinions of a specific entity or feature from reviews dataset. The opinions of other users help in performing the decision making process. This paper studies different methods that are aimed at performing sentiment analysis. These approaches vary from semantic based methods, machine learning, neural networks, and syntactical methods with each having its own strength. Although hybrid approach also exists, the main idea is to combine the strengths of two or more methods to increase the accuracy. A framework in which sentiment analysis is done by using the proposed word embedding and feature reduction techniques. Word embedding is a technique in which low-dimensional vector representation of words is provided. Feature reduction method employs a support vector machine (SVM) classifier. The framework will perform sentiment analysis of user opinions by using a machine learning approach and provides a recommendation system for the ease of decision making to users.