<![CDATA[The growth of transactions on e-commerce platforms generates a massive volume of unstructured customer review data. However, traditional Customer Relationship Management (CRM) models such as RFM often only focus on quantitative transaction data and ignore the emotional dimension contained in customer reviews. This study aims to analyze the relationship between purchase frequency and customer comment polarity through the integration of Text Mining and CRM Analytics approaches. The novelty offered is the development of a hybrid method that combines Lexicon Refinement-based sentiment extraction with the Random Forest algorithm to overcome rating bias in global e-commerce platform data (Kaggle). The proposed method includes the use of Natural Language Processing (NLP) techniques, topic modeling based on Latent Dirichlet Allocation (LDA), and sentiment analysis to extract polarity scores. The test results show that the initial lexicon model has limitations with an accuracy of 52.14% due to noise in neutral reviews (3-star rating). However, after optimization using the Random Forest algorithm and neutral data filtering, the classification accuracy increased significantly to 74.62%. These results prove that sentiment integration is able to provide more accurate loyalty mapping and help e-commerce management detect potential churn in the At-Risk customer segment.]]>
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