Review rating with joint classification and regression model

Abstract

Review rating is a sentiment analysis task which aims to predict a recommendation score for a review. Basically, classification and regression models are two major approaches to review rating, and these two approaches have their own characteristics and strength. For instance, the classification model can flexibly utilize distinguished models in machine learning, while the regression model can capture the connections between different rating scores. In this study, we propose a novel approach to review rating, namely joint LSTM, by exploiting the advantages of both review classification and regression models. Specifically, our approach employs an auxiliary Long-Short Term Memory (LSTM) layer to learn the auxiliary representation from the classification setting, and simultaneously join the auxiliary representation into the main LSTM layer for the review regression setting. In the learning process, the auxiliary classification LSTM model and the main regression LSTM model are jointly learned. Empirical studies demonstrate that our joint learning approach performs significantly better than using either individual classification or regression model on review rating.