<p>Access to reliable product reviews is crucial for consumers to make well-informed purchasing decisions. The process of extracting critical information from high-frequency, real-time, and unstructured text data also assists in ascertaining public opinion and but also can outline important indicators for business. Therefore, to improve the effectiveness of management decision-making in the context of high-frequency data, this paper constructs a three-way decision (TWD) evaluation model by introducing a novel interval-valued uncertainty function. First, this paper applies natural language processing to transform high-frequency comments and leverages the expressive power of probabilistic linguistic term sets to characterize complex uncertainties. This leads to a new concept known as probabilistic linguistic interval function. Second, by introducing clamped B-spline basis functions, high-frequency linguistic information is fitted into an interval function via an optimization model that accounts for individual decision-maker deviations. Third, to further analyze the large online review data, a cluster algorithm-based new membership degree matrix is constructed. The conditional probabilities are calculated to determine the classification regions, which provide the fundamental basis for characterizing and classifying alternatives. Based on favorable and unfavorable situations, the probabilistic linguistic TWD mechanism is constructed to process high-frequency aperiodic linguistic comments, enabling the division of alternatives into class domains depending on their utility values. Finally, a case study is used to demonstrate the proposed method, and parameter and comparative analyses are employed to validate its practicality and superiority.</p>