Reinforcement of Pre-trained Bert Architecture for the Detection of Spam Reviews

Konferenz: ISCTT 2021 - 6th International Conference on Information Science, Computer Technology and Transportation
26.11.2021 - 28.11.2021 in Xishuangbanna, China

Tagungsband: ISCTT 2021

Seiten: 6Sprache: EnglischTyp: PDF

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Liu, Yuxin (College of Information and Computer, Taiyuan University of Technology, Jinzhong, China)
Ning, Yansong; Wang, Li (College of Data Science, Taiyuan University of Technology, Jinzhong, China)

Spam reviews misguide consumers' decisions and may seriously influence transactions in the online markets. Existing detection modules mainly rely on tedious by-hand characteristics engineering, which expects much professional knowledge. Recent efforts utilize deep learning to extract semantics knowledge, and these methods cannot consider comprehensive potential semantics well. We propose a novel, reinforced pre-trained Bert architecture for spam review detection, including a multilayer bidirectional Trans-former encoder (BERT) and a 3-Layer fully connected network (3FCN) to capture comprehensive, essential, and meaningful information. Specifically, the BERT learns comprehensive semantic information, and the 3FCN reinforces the pre-trained Bert to obtain essential and meaningful information. Experiment results show that the proposed architecture has better detection performance.