This study aimed to explore consumer evaluations of beef by part, including its by-products. To achieve this, we analyzed online product reviews on Rakuten Ichiba (a major e-commerce site) from 2016 to 2019. We applied text mining analyses to these data in three stages: extraction of frequent words in the whole beef review to capture the overall characteristics; grouping of parts by correspondence analysis; and extraction of words that characterize each part to consider similarities and differences among parts or groups of parts. We found that for post-purchase evaluation criteria and consumption practices, several parts had some similarities, but there were also differences among several other parts and groups. Therefore, to further understand the needs of the consumer and promote the sales of beef, it would be advisable to consider the characteristics of each group of parts and each part individually.
