A comparative performance evaluation of neural network based approach for sentiment classification of online reviews

Abstract

The aim of sentiment classification is to efficiently identify the emotions expressed in the form of text messages. Machine learning methods for sentiment classification have been extensively studied, due to their predominant classification performance. Recent studies suggest that ensemble based machine learning methods provide better performance in classification. Artificial neural networks (ANNs) are rarely being investigated in the literature of sentiment classification. This paper compares neural network based sentiment classification methods (back propagation neural network (BPN), probabilistic neural network (PNN) & homogeneous ensemble of PNN (HEN)) using varying levels of word granularity as features for feature level sentiment classification. They are validated using a dataset of product reviews collected from the Amazon reviews website. An empirical analysis is done to compare results of ANN based methods with two statistical individual methods. The methods are evaluated using five different quality measures and results show that the homogeneous ensemble of the neural network method provides better performance. Among the two neural network approaches used, probabilistic neural networks (PNNs) outperform in classifying the sentiment of the product reviews. The integration of neural network based sentiment classification methods with principal component analysis (PCA) as a feature reduction technique provides superior performance in terms of training time also.