Many real-world decision-making problems are inherently multi-criteria and rely on linguistic and subjective judgments that are typically expressed as textual statements. In such situations, classical methods are often unable to accurately model textual opinions and the uncertainty present in data, particularly when user evaluations become inconsistent or contradictory. This study proposes a 2-tuple fuzzy logic–based approach for ranking alternatives in multi-criteria decision-making problems using Persian textual reviews from Iranian users as input.In the proposed method, linguistic evaluations extracted from text are transformed into processable fuzzy values, and the interrelationships among criteria are considered in determining their weights using Shannon entropy. Finally, the ranking of alternatives is performed through the Analytic Hierarchy Process (AHP) based on a pairwise comparison matrix. In this way, both user evaluations and the interactions among criteria are simultaneously incorporated into the decision-making process.The final output is a prioritized ranking of alternatives that reflects users’ decision logic in uncertain and multi-criteria environments. As a case study, the Iranian automobile market is investigated, where Persian-speaking users expressed their opinions about six cars in textual form. The results show that the proposed fuzzy-based method effectively analyzes textual data while preserving ambiguity and uncertainty, enabling more accurate decision-making and reliable extraction of user preferences.