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Co-attention Based Deep Model with Domain-Adversarial Training for Spam Review Detection

Published: 10 May 2022 Publication History

Abstract

Douban has become one of the most popular Chinese film review platforms and has been attacked by various spammers. In our research based on real data from Douban, we found that many spammers spontaneously form different groups, which have different targets and have different effects on movies. In this paper, by dividing reviews into four categories, namely, true positive reviews, spam positive reviews, true negative reviews, and spam negative reviews, we design a co-attention based neural network to fuse multiple features to classify reviews. In order to improve the robustness of the detection model, we adopt the idea of domain-adversarial training. In the domain-adversarial training method, we use real data and noisy data for model training, and the model is indiscriminative for whether the data source is real data or noisy data. The experimental results show that our proposed domain-adversarial training method can improve both the best classification performance and robustness.

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  • (2023)Tsetlin Machine for Sentiment Analysis and Spam Review Detection in ChineseAlgorithms10.3390/a1602009316:2(93)Online publication date: 8-Feb-2023

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ICNCC '21: Proceedings of the 2021 10th International Conference on Networks, Communication and Computing
December 2021
146 pages
ISBN:9781450385848
DOI:10.1145/3510513
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 10 May 2022

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  1. Domain-adversarial training
  2. Feature fusion
  3. Spam review detection

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  • (2023)Tsetlin Machine for Sentiment Analysis and Spam Review Detection in ChineseAlgorithms10.3390/a1602009316:2(93)Online publication date: 8-Feb-2023

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