Name
Abstract | ||
|---|---|---|
With more consumers using online opinion reviews to inform their service decision making, opinion reviews have an economical impact on the bottom line of businesses. Unsurprisingly, opportunistic individuals or groups have attempted to abuse or manipulate online opinion reviews (e.g., spam reviews) to make profits and so on, and that detecting deceptive and fake opinion reviews is a topic of ongoing research interest. In this paper, we explain how semi-supervised learning methods can be used to detect spam reviews, prior to demonstrating its utility using a data set of hotel reviews. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ACCESS.2017.2655032 | IEEE ACCESS |
Keywords | Field | DocType |
Online review spam,semi-supervised learning,unlabeled reviews,PU learning,Co-training,EM algorithm,label propagation and spreading | Data science,Semi-supervised learning,Algorithm design,Computer science,Support vector machine,Prediction algorithms,Artificial intelligence,Machine learning | Journal |
Volume | ISSN | Citations |
5 | 2169-3536 | 6 |
PageRank | References | Authors |
0.57 | 32 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jitendra Kumar Rout | 1 | 18 | 2.95 |
| Anmol Dalmia | 2 | 6 | 0.57 |
| Kim-Kwang Raymond Choo | 3 | 4103 | 362.49 |
| Sambit Bakshi | 4 | 123 | 23.10 |
| Sanjay Kumar Jena | 5 | 101 | 14.37 |