Name
Abstract | ||
|---|---|---|
Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous customers. However, it is possible to superficially boost or hinder some businesses through posting counterfeit and fake reviews. This paper explores a natural language processing approach to identify fake reviews. We present a detailed analysis of linguistic features for distinguishing fake and trustworthy online reviews. We study 15 linguistic features and measure their significance and importance towards the classification schemes employed in this study. Our results indicate that fake reviews tend to include more redundant terms and pauses, and generally contain longer sentences. The application of several machine learning classification algorithms revealed that we were able to discriminate fake from real reviews with high accuracy using these linguistic features. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICMLA51294.2020.00063 | 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) |
Keywords | DocType | ISBN |
fake review,deception detection,machine learning,linguistic features | Conference | 978-1-7281-8471-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faranak Abri | 1 | 0 | 0.34 |
| Luis Felipe Gutiérrez | 2 | 0 | 0.34 |
| Akbar Siami Namin | 3 | 0 | 1.35 |
| Keith S. Jones | 4 | 0 | 0.34 |
| David Sears | 5 | 1 | 2.80 |