Response Generation to Low-Rated Reviews Combined with Sentiment Analysis

Abstract

For users of hotel booking sites, customer reviews and responses to them from the facilities are important factors for the users’ decision making. Therefore, a system that helps facility staff write back to their customers is highly needed. However, generating adequate responses to negative reviews, which are much fewer than positive ones, is difficult with machine learning approaches, which often assume balanced data. In this study, we attempt to generate responses to negative reviews by controlling responses by using sentiment analysis. We construct a system that combines a sequence-to-sequence model based on GPT-2 and a sentiment classifier based on BERT, and evaluated the system using review data from Rakuten Travel. Through objective evaluation, we show that the system is able to generate more human-like responses. Through subjective evaluation, we show that the model considering sentiment is capable of generating responses that are more appropriate to negative reviews.