Summary of Finding Fake Reviews in E-commerce Platforms by Using Hybrid Algorithms, By Mathivanan Periasamy et al.
Finding fake reviews in e-commerce platforms by using hybrid algorithms
by Mathivanan Periasamy, Rohith Mahadevan, Bagiya Lakshmi S, Raja CSP Raman, Hasan Kumar S, Jasper Jessiman
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ensemble approach for sentiment analysis combines Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree classifiers to predict fake reviews with superior accuracy and robustness. This hybrid architecture leverages the strengths of each model while mitigating weaknesses, showcasing enhanced predictive performance and adaptability to real-world linguistic patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to analyze emotions in text by combining different machine learning models. These models are used to find fake reviews online, which is important for making sure people don’t spread false information. The combined approach does better than using just one model alone and can handle different types of language. This could be useful for social media platforms and e-commerce websites. |
Keywords
* Artificial intelligence * Decision tree * Machine learning * Support vector machine
