Customer segmentation is a prerequisite for evidence-based decision-making and for designing marketing strategies aligned with the heterogeneous characteristics of customer groups. The traditional RFM model, based on Recency, Frequency, and Monetary value, has been widely used for this purpose; however, it focuses on transactional indicators and does not capture the emotional and experiential information expressed in customer reviews. This study develops the four-dimensional RFMSA framework, which integrates RFM with sentiment analysis to incorporate behavioral and emotional information into a unified segmentation process. Following data preprocessing and exploratory analysis, RFM indicators were calculated and customers were initially segmented using predefined thresholds. Logistic regression was then applied to examine the reproducibility and internal consistency of the RFM groups. Since the target labels were defined by the same RFM thresholds rather than clustering outputs, the results were interpreted as a consistency check rather than independent validation. Subsequently, sentiment scores were extracted from customer reviews using the VADER sentiment analysis tool and aggregated at the customer level. The aggregated sentiment score was normalized to the [0,1] interval and incorporated as a fourth dimension alongside the three RFM indicators to construct the RFMSA framework. Customers were then clustered based on the four RFMSA features using the K-means algorithm, with the number of clusters assessed using the elbow method and silhouette index. Analysis of variance (ANOVA) and Tukey’s test were used to examine differences in sentiment across the resulting groups. The findings indicate that incorporating sentiment provides information not captured by traditional RFM indicators and enables behavioral and emotional characteristics to be considered simultaneously. In particular, customers with high monetary value and negative sentiment, as well as those with moderate monetary value and positive sentiment, could be distinguished through the integrated framework. Overall, RFMSA provides a structured approach for combining transactional and textual customer information, offering a more differentiated representation of customer profiles and supporting the development of marketing strategies aligned with the characteristics of different customer groups.