Model Comparison in Sentiment Analysis: A Case Study of Amazon Product Reviews

Abstract

Sentiment analysis is essential in NLP, especially in businesses because it can improve customer services. This paper focuses on a particular case of sentiment analysis, a case study of Amazon reviews of books on kindle. Firstly, this paper applies several non-deep-learning algorithms including Logistic Regression, Naïve Bayes, Support Vector Machine, Convolutional Neural Network, and Recurrent Neural Network, and compares their accuracies. Especially, for deep learning methods, this paper studies the slope of accuracies concerning the number of hidden layers. Secondly, as a multi-class text classification problem, the product review data set has five labels ranging from one star to five stars, a new method called Hybrid Sequential Binary Classification (HSBC) is introduced in this paper, which improves the behavior of classical binary classifiers on a multi-class text classification problem. Moreover, a comparison of HSBC and multi-class classification models is presented.