Name
Abstract | ||
|---|---|---|
Spam reviewers are becoming more professional. The common approach in spam reviewer detection is mainly based on the similarities among reviews or ratings on the same products. Applying this approach to professional spammer detection has some difficulties. First, some of the review systems start to set some limitations, e.g., duplicate submissions from a same id on one product are forbidden. Second, the professional spammers also greatly improve their writing skills. They are consciously trying to use diverse expressions in reviews. In this paper, we present a novel model for detecting professional spam reviewers, which combines posting frequency and text sentiment strength by analyzing the writing and behavior styles. Specifically, we first introduce an approach for counting posting frequency based on a sliding window. We then evaluate the sentiment strength by calculating the sentimental words in the text. Finally, we present a linear combination model. Experimental results on a real dataset from Dianping.com demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-53917-6_26 | ADMA (2) |
Keywords | Field | DocType |
posting frequency,professional review spammer detection,text sentiment strength | Data mining,Linear combination,Sliding window protocol,Expression (mathematics),Computer science,Writing skills,Spamming | Conference |
Volume | Issue | Citations |
8347 LNAI | PART 2 | 1 |
PageRank | References | Authors |
0.37 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junlong Huang | 1 | 1 | 0.71 |
| Tieyun Qian | 2 | 177 | 28.81 |
| Guoliang He | 3 | 75 | 12.73 |
| Ming Zhong | 4 | 20 | 5.59 |
| Qingxi Peng | 5 | 9 | 0.84 |