Exploring groups of opinion spam using sentiment analysis guided by nominated topics
Exploring groups of opinion spam using sentiment analysis guided by nominated topics
复制标题
使用指定主题引导的情感分析来探索垃圾评论组
DOI：
10.1016/j.eswa.2021.114585
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发表时间：
2021-01-30
影响因子：
8.5
通讯作者：
Zhang, Pengpeng
中科院分区：
文献类型：
--
作者：
Li, Jiandun;Lv, Pin;Zhang, Pengpeng
Currently, it is common to see untruthful opinions (also known as review spam, fraud or shilling attack) that resemble each other explicitly or implicitly across multiple business-to-customer websites or opinion sharing communities. Unfortunately, these fake recommendations can be fabricated by individual spammers or results of a manipulation campaign. Considering its severe harmfulness in influencing a product?s reputation, grouped spam is more urgent to detect than individual fraud. Most state-of-the-art techniques of labeling grouped spam, e. g., Frequent Itemset Mining (FIM) or Latent Dirichlet Allocation (LDA), are completely unsupervised and incapable of making good use of officially recommended topics, such as appearance, speed and standby are three suggested aspects along a cell phone product in JD.com. In this paper, we introduce a novel approach based on aspect-oriented sentiment mining that can identify spam groups supported by nominated topics. Experiments show that our method is effective and outperforms several state-of-the-art solutions with statistical significance on two metrics, content duplication and burstiness of time.
