An attention-based unsupervised adversarial model for movie review spam detection

TL;DR

This paper introduces an unsupervised, attention-based adversarial model for detecting spam in movie reviews, leveraging statistical features and genre-specific review styles to improve accuracy without manual labeling.

Contribution

It presents a novel unsupervised model combining attention mechanisms and generative adversarial networks for movie review spam detection, addressing limitations of manual feature extraction.

Findings

Superior performance on Douban movie reviews

Effective genre-specific review style learning

Unsupervised approach reduces manual effort

Abstract

With the prevalence of the Internet, online reviews have become a valuable information resource for people. However, the authenticity of online reviews remains a concern, and deceptive reviews have become one of the most urgent network security problems to be solved. Review spams will mislead users into making suboptimal choices and inflict their trust in online reviews. Most existing research manually extracted features and labeled training samples, which are usually complicated and time-consuming. This paper focuses primarily on a neglected emerging domain - movie review, and develops a novel unsupervised spam detection model with an attention mechanism. By extracting the statistical features of reviews, it is revealed that users will express their sentiments on different aspects of movies in reviews. An attention mechanism is introduced in the review embedding, and the conditional generative adversarial network is exploited to learn users' review style for different genres of movies. The proposed model is evaluated on movie reviews crawled from Douban, a Chinese online community where people could express their feelings about movies. The experimental results demonstrate the superior performance of the proposed approach.