Nowadays, consumers are inclined to issue their opinions for merchandise in the era of Web 2.0. As a result, numerous review comments about different products or services are accumulated on various websites every day. It has been found that to manipulate customer opinions, some dealers created the review comments in order to exaggerate the advantages of their own products or defame rival’s reputation. This study strived to identify the negative fake review comments which were falsely created and aimed at attacking targeted products. The method created three word banks, namely, vagueness, and positive and negative attacks. The number of these words appearing in each review comments were calculated and applied to build logistic regression models. The experiment was conducted with true hostel review comments taking from “TripAdvisor” and the comparison group “Fake reviews” on Amazon Mechanical Turk. In the case where the ratio of fake and true review comments are10% in the training data, the proposed method reached 100%, 51.5% and 3% of precision, accuracy and recall, respectively. When the ratio is 50%, the method could reach 64%, 64%, 64% of precision, accuracy and recall respectively. The performance is better than the benchmark method which based on LIWC and SVM.