SARWAS: Deep ensemble learning techniques for sentiment based recommendation system

Abstract

Identifying user preferences is a complex operation, which makes its automa- tion challenging, and existing recommendation systems that rely on one of the parameters ratings or reviews are incapable of performing effectively. In this work, an ensemble based deep learning method is used to develop a recommen- dation model in which both ratings and reviews are analyzed simultaneously. The first step is to identify the features required in our work which is reviews and ratings of a product. Next step is to conduct sentiment analysis on reviews to get numerical values for subsequent processing which gives output in terms of polarity and subjectivity, where polarity identifies the emotions expressed in reviews ranging from −1 to +1 and subjectivity identifies the relevance of par- ticular review. Reviews and ratings as features used in this work as inputs for ensemble based deep neural network. For this, both the inputs need to be in same range so scaling is done. The recommendation method used in this work is based on deep learning which employs back propagation neural networks with many hidden layers and varying nodes, facilitating rapid learning. In this pa- per, few selected representative deep learning architectures with varied amounts of layers concealed to improve the learning capability of this model. However, model of the recommendation system can yet be improved, such as the inabil- ity to explain the deep learning recommendation system, which diminishes its credibility. We assessed our work based on hidden layer architectural variations using measures such as precision, loss, and execution time.