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 en_US
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20180613~20180615
通訊作者
國別 CHN
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)
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/115252 )