Sentiment Analysis Based on Feature Selection and Machine Learning Techniques

Abstract

This research focuses on sentiment analysis of amazon food review dataset. For this work, it used some machine learning algorithms but specifically used sentiment analysis and LSTM. It was implemented by some machine learning algorithm with sentiment analysis. People are now a day more depends on restaurant food because of their busy life. For this they taste many kinds of restaurants and without knowledge they don’t know where they can get good food with good service. They don’t know which is good or which is bad and sometimes the reviews are so confusing that they do not understand. To overcome the above problems researchers have used machine learning algorithms to classify positive or negative food review with binary classification. In this study it discussed about long short term memory (LSTM) and four machine learning algorithm to solve this problem it was also use aspect based sentiment analysis. There are a lot of approaches developed for binary classification but it used Naïve Bayes (Bernoulli), Perceptron, Decision tree, Logistic regression, Long short term memory(LSTM). Recurrent Neural Networks(RNN) have exposed as widely used architectures and are united with sequence-based models. The main research aims to develop this machine learning algorithm to gives the best result for binary classification in restaurant food review. The purpose of this research is binary classification and sentiment analysis. To classify text, it has been used Amazon food review data set. It has been used some machine learning algorithms. It has been divided the work into three stages, these were data preprocessing, sentiment analysis and binary classification.