This paper proposes a method for constructing personal value-based user models from review texts using LLM (Large Language Models). The RMrate (Rating Matching Rate) has been proposed as a metric to quantitatively assess the intensity of user preferences towards item attributes when selecting items, and has been applied to personal value-based models. RMrate is defined as the proportion of cases where the evaluation polarity of an item’s attributes matches the polarity of the overall evaluation: attributes of high RMrate are considered to have a strong influence on user’s decision making. While its effectiveness in information recommendation has been demonstrated, existing methods required explicit attribute evaluations. To address this issue, the proposed method calculates RMrate by applying LLM to extract the evaluation polarity of item’s attributes mentioned in reviews through prompting. Additionally, based on the assumption that LLM can determine whether attributes are mentioned in review texts before judging their evaluation polarity, this paper also proposes to extend RMrate by adding a term representing the frequency of attribute mentions. In this paper, experiments using movies as target items are conducted to evaluate the accuracy of polarity extraction and its effectiveness for recommendation. The effectiveness of the proposed extended RMrate is also shown with experiments.