Abstract
In recent years, many methods have been studied to analyze large amounts of review data posted by users in order to improve products and services. Most of these methods are mainly studies that attempt to extract some kind of information from user reviews using various machine learning techniques, and most of them do not provide an analysis method that clearly prioritizes quality improvement by considering the characteristics of each factor and the customer's viewpoint. In contrast, studies focusing on quality improvement have proposed a review analysis that introduces the concept of the Kano model, which classifies quality factors into "attractive quality" and other categories based on changes in customer satisfaction depending on their presence or absence. However, these studies have the problem that they cannot analyze at the word level or focus on only some words. In this study, we propose a method to analyze all words in a review sentence and classify quality factors. Specifically, by combining LDA learned using review sentences and logistic regression, we quantify the influence of each word that appears in the review sentences on the evaluation value. Through analysis of real data, we show that the proposed method can be used for detailed word-level analysis and heuristic quality factor extraction.
