關鍵字查詢 | 類別:會議論文 | | 關鍵字:Detecting Spamming Reviews Using Long Short-term Memory Recurrent Neural Network Framework

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序號 學年期 教師動態
1 106/2 資管系 戴敏育 副教授 會議論文 發佈 Detecting Spamming Reviews Using Long Short-term Memory Recurrent Neural Network Framework , [106-2] :Detecting Spamming Reviews Using Long Short-term Memory Recurrent Neural Network Framework會議論文Detecting Spamming Reviews Using Long Short-term Memory Recurrent Neural Network FrameworkChih-Chien Wang; Min-Yuh Day; Chien-Chang Chen; Jia-Wei LiouFake Review;Deep Learning;Neural Network;Long Short-term Memory (LSTM);Recurrent Neural Network (RNN)Proceedings of the 2nd International Conference on E-commerce, E-Business and E-Government (ICEEG 2018)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.
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