教師資料查詢 | 類別: 會議論文 | 教師: 戴敏育 Min-Yuh Day (瀏覽個人網頁)

標題:Detecting Spamming Reviews Using Long Short-term Memory Recurrent Neural Network Framework
學年106
學期2
發表日期2018/06/13
作品名稱Detecting Spamming Reviews Using Long Short-term Memory Recurrent Neural Network Framework
作品名稱(其他語言)
著者Chih-Chien Wang; Min-Yuh Day; Chien-Chang Chen; Jia-Wei Liou
作品所屬單位
出版者
會議名稱The 2nd International Conference on E-commerce, E-Business and E-Government (ICEEG 2018)
會議地點Hong Kong, China
摘要Some unethical companies may hire workers (fake review spammers) to write reviews to influence consumers' purchasing decisions. However, it is not easy for consumers to distinguish real reviews posted by ordinary users or fake reviews post by fake review spammers. In this current study, we attempt to use Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) framework to detect spammers. In the current, we used a real case of fake review in Taiwan, and compared the analytical results of the current study with results of previous literature. We found that the LSTM method was more effective than Support Vector Machine (SVM) for detecting fake reviews. We concluded that deep learning could be use to detect fake reviews.
關鍵字Fake Review;Deep Learning;Neural Network;Long Short-term Memory (LSTM);Recurrent Neural Network (RNN)
語言英文(美國)
收錄於
會議性質國際
校內研討會地點
研討會時間20180613~20180615
通訊作者
國別中國
公開徵稿
出版型式
出處Proceedings of the 2nd International Conference on E-commerce, E-Business and E-Government (ICEEG 2018)
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