English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Opinion spam has become a widespread problem in social media, where hiredspammers write deceptive reviews to promote or demote products to mislead theconsumers for profit or fame. Existing works mainly focus on manually designingdiscrete textual or behavior features, which cannot capture complex semanticsof reviews. Although recent works apply deep learning methods to learnreview-level semantic features, their models ignore the impact of theuser-level and product-level information on learning review semantics and theinherent user-review-product relationship information. In this paper, wepropose a Hierarchical Fusion Attention Network (HFAN) to automatically learnthe semantics of reviews from the user and product level. Specifically, wedesign a multi-attention unit to extract user(product)-related reviewinformation. Then, we use orthogonal decomposition and fusion attention tolearn a user, review, and product representation from the review information.Finally, we take the review as a relation between user and product entity andapply TransH to jointly encode this relationship into review representation.Experimental results obtained more than 10\% absolute precision improvementover the state-of-the-art performances on four real-world datasets, which showthe effectiveness and versatility of the model.

Learning review representations from user and product level information for spam detection