[1P5-GS-6] Language media processing: applications

[1P5-GS-6-02] Analysis of Consumers' Feedback on a Japanese EC Site Focusing on the Relation between Review Text and Rating

〇Yoshiyuki Kobayashi¹, Takafumi Koshinaka¹ (1. Yokohama City University)

[[Online]]

Keywords:Neural networks, Transformer, Sentiment analysis

Many EC sites publish reviews, which are feedback from purchasers of each product, to help customers make purchase decisions and improve site sales. A review consists of review text, which is an impression of the product, and a numerical rating, such as a five-point scale. It is beneficial for both the site operator and the customers if this information is properly provided. In this study, we conduct a regression analysis to predict the rating from the review text. An experiment using 60,000 reviews from Rakuten Ichiba shows that the Transformer model can better predict the five levels of ratings given by customers compared to linear prediction models and RNN-based models. In addition, while the ratings given by the consumers and the review text generally agree, there are cases where they do not, and qualitative analysis confirms the possibility that the predictive model can detect and correct such discrepancies.