An Efficient Ensemble Approach for Fake Reviews Detection
Opinion spam on online restaurant review sites is a major problem as the reviews influence the users? choice to visit or not a restaurant. In this project, we will address the problem of detecting genuine and fake reviews in restaurant online reviews. We propose a fake review detection technique com
Project Title
An Efficient Ensemble Approach for Fake Reviews Detection
Project Area of Specialization
Project Summary
Opinion spam on online restaurant review sites is a major problem as the reviews influence the users’ choice to visit or not a restaurant. In this project, we will address the problem of detecting genuine and fake reviews in restaurant online reviews. We propose a fake review detection technique comprising data preprocessing, detection and ensemble learning that learns the reviews and their features to filter out the fake reviews. Initially, decision trees, random forests and logistics regression algorithms will be implemented. Finally, a hybrid ensemble model from the two classifiers is built to detect genuine and fake reviews. Precision and recall will be used for performance measures.
Project Objectives
- To detect the fake reviews on the online store through a hybrid approach
- To put in place the best technique possible for detecting false reviews.
- To inform consumers if every single review is genuine or not to make the most effective use of money
Project Implementation Method
The proposed methodology consists of the following steps:
1. Data Collection
2. Data preprocessing and preparation
3. Data summarization
4. Training model (Adaboost, Random Forest, and Logistic Regression classifier)
5. Grid Search Algorithm for Hyperparameters Optimization
6. Performance evaluation (Precision-Recall, F1-score)
Benefits of the Project
1. This project is research-oriented and based on machine learning methods. It will help the researcher to find out the more optimal solutions to this problem.
2. Reviews are considered one of the important aspects of different online platforms. The quality of reviews also plays an important role in certain purchases on an online system. Therefore, different scientists have devised different strategies to detect fake reviews from the provided dataset. Moreover, detecting fake reviews also aids to identify fake accounts. In this project, we have successfully applied an ensemble learning approach to detect fake reviews which will useful for online customers and restaurants.
Technical Details of Final Deliverable
Because this project is based on research, so we will use a confusion matrix for evaluation. Precision, recall and F1-score will be measured for each algorithm. Also, we will publish a research paper in an impact factor journal.
Final Deliverable of the Project
Core Industry
Other Industries
Core Technology
Other Technologies
Sustainable Development Goals
Required Resources
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| SSD | Equipment | 1 | 5000 | 5000 |
| RAM | Equipment | 1 | 2500 | 2500 |
| GPU | Equipment | 1 | 38000 | 38000 |
| CPU | Equipment | 1 | 15000 | 15000 |
| Total in (Rs) | 60500 |
If you need this project, please contact me on contact@adikhanofficial.com
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