  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.
