An Estimation Model of Intrinsic Evaluation Ratings by Customer Reviews Based on BERT Feature Extraction

In recent years, many product reviews have been posted on e-commerce sites. These review data contain the impressions and requests of users who have purchased and used the products and have a direct impact on the purchasing behavior of other users. On the other hand, manufacturers need to analyze review data not only to understand users' needs but also to understand the problems of existing products. In addition, since these review data include the evaluation values of the users who submitted the data, the analysis of the review data is valuable as direct product evaluation information by the users. Although users are generally expected to give a rating that matches the content of the review, some users are dissatisfied with the product but give a high rating. Conversely, there are some users who are satisfied with the product but give an intermediate rating in a cursory way, so the content of the review and the rating do not necessarily match. In such cases, judging the evaluation of a product by focusing on the evaluation value may result in a product evaluation that deviates from the actual evaluation by the user. Therefore, this study introduces BERT (Bidirectional Encoder Representations from Transformers) and sentiment analysis methods, which have recently shown effectiveness in natural language processing. We propose a method for estimating evaluation values that consider the user's emotional content expressed in the review sentences. Furthermore, we apply the proposed method to the real review data and demonstrate its usefulness.