[2N1-GS-4] Web intelligence:

[2N1-GS-4-01] Conversion Method for Review Scores Based on Information Content and Similarity Analysis with Order Quantity

〇Ryoichiro Yamazaki¹, Tetta Noshiro¹, Wataru Sato¹, Yuki Yamagishi^1,2,3, Mai Izumi², Takahito Takabayashi² (1. Shizuoka Institute of Science and Technology, 2. Ryohin Keikaku Co., Ltd., 3. RIKEN)

Keywords:Review site, Time series analysis, Self-information content

The advancement of natural language processing models has made text and image generation accessible to everyone. It has been noted that distinguishing between reviews generated by these models and those written by humans is challenging, casting doubt on the future reliability of such reviews. Furthermore, changes in legislation could subject individuals who post reviews to penalties, suggesting that the reliability of review texts is already in question. Therefore, this study aims to convert user review scores into an objective indicator using the information content of scores based on the distribution of user review scores, employing only posted scores for analysis. The study also visualizes this data as time series and compares it with data on online store order quantities. Using Dynamic Time Warping, the study measures the similarity between average review scores, online store order quantities, and the proposed evaluation value. The analysis indicates a significant correlation between the proposed evaluation value and online store order quantities across various product categories, suggesting its potential as a new evaluation metric. Additionally, the method can be applied to various review sites without being limited by the scoring system.