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Fake review identification and utility evaluation model using machine learning 2.3. Sentiment analysis processing modeling 3.5. Logistic regression In Equation (1), b0 is the intercept of the model, and bi (i = 0, 1, 2, ..., n) is the slope coefficient of the logistic regression model. Figure 4. Flowchart of the study. 5. Experiments and results As a result of the unsupervised learning of this study, there were reviews suspected of being misclassified among the reviews determined to be fake reviews, and among the reviews determined to be non-fake reviews, there were reviews suspected to be fake reviews. In addition, there was ambiguity in setting the criteria for classification. Through this, it was judged that the accuracy of fake review detection through unsupervised learning was poor. Myung, J., Lee, D., and Lee, S. (2008). A Korean product review analysis system using a semi-automatically constructed semantic dictionary. J. KIISE: Softw. Appl. 35, 392–403. camera bag cyber mondayprada bag cheap
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