<p>The use of online reviews for ranking products has gained increasing interest in recent years. Yet, previous research has mainly single data modal in online reviews and do not consider consumer heterogeneity. Furthermore, the heterogeneity of consumers requires product ranking to be more targeted. To address these problems, this study proposes a multimodal data-driven personalized decision-making model for supporting purchase decisions of heterogeneous consumers. First, multimodal data (i.e., unstructured textual review data and structured ratings data) is crawled and preprocessed, then a new sentiment analysis process based on prospect theory is proposed. Second, a multimodal data fusion process is developed, which consists of ratings compression and information fusion. Third, this study presents a product ranking model for heterogeneous consumers. In this ranking model, heterogeneous consumers are divided into three categories according to consumers’ familiarity with the product, three methods related to criteria weight determination are developed to depict the personalization of heterogeneous consumers, and regret theory is used to characterize consumers’ decision-making psychology. Finally, a case study on helping consumers purchase new energy vehicles (NEVs) is provided to verify the feasibility and effectiveness of the product ranking model. In addition, the results of the benchmark analysis, the sensitivity analysis and the comparative analysis indicate that our personalized decision-making model is robust and effective for supporting purchase decisions of heterogeneous consumers.</p>