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Subsequently, the K-Nearest Neighbors (KNN) algorithm is applied to the dataset to investigate its performance, revealing that the Manhattan distance is the optimal distance formula for researching this problem using the KNN algorithm. The study explicitly selects frequent words chosen by individual users as features for calculation. During the computation process, grid search is employed to find the optimal parameters, and a model is created using these optimal parameters. The paper then applies the Naive Bayes algorithm to the dataset, comparing the strengths and weaknesses of different types of Naive Bayes methods. 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