Name
Abstract | ||
|---|---|---|
In the ongoing era of flourishing e-commerce, people prefer online purchasing products and services to save time. These online purchase decisions are mostly influenced by the reviews/ opinions of others who already have experienced them. Malicious review spam. This study aims to evaluate the performance of ensemble learning on review spam detection with selected features extracted from real and semi-real-life datasets. We study various performance metrics including Precision, Recall, F-Measure, and Receiver Operating Characteristic (RoC). Our proposed ensemble learning module (ELM) with ChiSquared feature selection technique outperformed all others with 0.851 Precision. (c) 2019 The Authors. Published by Atlantis Press SARL. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.2991/ijcis.2019.125905655 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS |
Keywords | DocType | Volume |
Review spam, Ensemble learning module, Positive polarity, Negative polarity | Journal | 12 |
Issue | ISSN | Citations |
1 | 1875-6891 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faisal Khurshid | 1 | 4 | 3.07 |
| Yan Zhu | 2 | 1 | 1.72 |
| Zhuang Xu | 3 | 0 | 0.34 |
| Mushtaq Ahmad | 4 | 63 | 11.30 |
| Muqeet Ahmad | 5 | 1 | 1.03 |