隨著網路購物的普及，消費者在買產品前逐漸提高對評論 依賴度。當越來容易被張貼於網路，產生捏造的評論欺騙消費者可能性。多研究開始關心虛假評論，但都僅限於單一領域而唯有跨辨別的究，在辨別跨領域評論效果不如預期。 故本研究設計出可檢測跨領域辨別真實與虛假評論的通用 性方法，使Stimuli-Organism-Response (S-O-R) 框架作為虛假評論 字詞的理構，並以此起點推架作為虛假評論 字詞的理構，並以此起點推有判斷力的字詞類別。藉由此發展出可以跨領域辨真實與虛假評論法則。本研究所發展之判斷法結果顯示，可以有效的辨別真實與虛假評論並在跨領域應用達到八成的精準度(Accuracy)。;With the rapid development of online shopping, consumers increasingly rely on user-generated online reviews to facilitate decision makings. As a result, deceptive review comments have been posted to elude readers. Most research concerns of this issues built up rules based on text data banks closely related to specific domains. To the best of our knowledge, only one study has strived to construct decision models to detect deceiving posts. However, the accuracy is only a bit higher than 50% when being applied to detect cross domain reviews. This study devising a general method to detect deceptive review comments, by adapting S-O-R framework. On the basis of the SOR framework, this study identify discriminant categories of words to detect deceptive reviews. The experiment result showed that the accuracy of the proposed method can reach 80 percent.
