![Reference
[1]. Dhairya Patel, Aishwerya Kapoor and Sameet Sonawane “Fake review detection using opinion mining” International Research journal of Engineering and
technology (IRJET) , volume 5, issue 12,Dec 2018.
[2]. Jitendra kumar Rout, Amiya Kumar Dash and Niranjan Kumar Ray “Framework for Fake Review Detection: Issues and Challenges” IEEE Xplore
ISBN:978-1-7281-0259-7/18 2018.
[3]. Nidhi A. Patel and Prof. Rakesh Patel “A Survey on Fake Review Detection using Machine Learning Techniques” IEEE Xplore ISBN: 978-1-5386-6947-
1/18 2018.
[4]. Sanjay K.S and Dr.Ajit Danti “Detection of fake opinions on online products using Decision Tree and Information Gain” IEEE Xplore ISBN: 978-1-5386-
7808-4 2019.
[5]. Syed Mohammed Anas and Santoshi Kumari “Opinion Mining based Fake Product review Monitoring and Removal System” IEEE Xplore ISBN: 978-1-
7281-8501-9 2021.](https://image.slidesharecdn.com/shivappt-240309081134-86640dee/85/Shiva-pptvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv-pptx-15-320.jpg)
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- 2. Agenda Overview Working of Fake Review detection Fake Review Survey Using Machine learning Approach Fake Review detection Using Naïve Bias Fake Review detection Using Random Forest Classifier Fake Review detection using Decision tree Scope of Growth and Disadvantages Conclusion Reference
- 3. Overview The reviews on a product may be positive or negative, the negative reviews will attract the customers more than a positive review. These fake reviews can affect any business which leads financial profit or loses. Due to the financial reasons many fake reviews will be appeared in these websites. The company owners will intentionally motivate some of the people to write the fake reviews to improve their business towards another product. The focus of this research is to create an environment of online E-commerce industry where consumers build trust in a platform where the products they purchase are genuine and feedbacks posted on these websites/applications are true So our Final aim in this is to implement best approach available for detection of fake reviews using opinion mining (sentiment analysis) techniques. To let users, know if each individual review is trustworthy or not for efficient use of money from users side.
- 4. Working of Fake Review detection 1. Preprocessing 2. Dataset 3. Feature Extraction 4. Model Training
- 5. Fake Review Survey Using Machine learning Approach Figure 3.11: Machine Learning based Fake Review Detection
- 6. Fake Review Survey Using Machine learning Approach World Wide Web has drastically changed the way of sharing the opinions. Online reviews are comments, tweets, and posts, opinions on different online platforms like review sites, news sites, e-commerce sites or any other social networking sites 1. Untruthful reviews 2. Reviews on brands 3. Review Centric Approach 4. Centric Approach 5. Product Centric Approach
- 7. Fake Review Survey Using Machine learning Approach Data Collection Data pre-processing Feature Extraction and selection Classifier model construction and testing
- 8. Working of Fake Review detection Figure 3.1: Model Diagram for fake review detection
- 9. Fake Review Detection Using Naïve Bias Figure 3.3: Naïve Bias Equation
- 10. Naïve Bias Vs. Random Forest Classifier Figure 3.4: Model Diagram for fake review detection
- 11. Fake Review detection using Decision tree Figure 3.6: Machine Learning based Fake Review
- 12. Application:
- 13. Scope of Growth and Disadvantages No Fixed Algorithm we need to choose a Algorithm based on Criteria and this is a manual task In future work, hybrid models and new models can be tried for the fake review detection model. Process is slow we can increase its speed by using Graphical Processing Unit Need for constant evolution of methods because humans always find smarter ways to cheat the system
- 14. Conclusion Identifying fake reviews from a large dataset is challenging enough to become an important research problem. Business organizations, specialists and academics are battling to find the best system for opinion spam analysis. The most important part of an algorithm is its efficiency. Efficiency is not just about execution time. The efficiency of an algorithm is about the time taken for training the model and the time taken for the prediction
- 15. Reference [1]. Dhairya Patel, Aishwerya Kapoor and Sameet Sonawane “Fake review detection using opinion mining” International Research journal of Engineering and technology (IRJET) , volume 5, issue 12,Dec 2018. [2]. Jitendra kumar Rout, Amiya Kumar Dash and Niranjan Kumar Ray “Framework for Fake Review Detection: Issues and Challenges” IEEE Xplore ISBN:978-1-7281-0259-7/18 2018. [3]. Nidhi A. Patel and Prof. Rakesh Patel “A Survey on Fake Review Detection using Machine Learning Techniques” IEEE Xplore ISBN: 978-1-5386-6947- 1/18 2018. [4]. Sanjay K.S and Dr.Ajit Danti “Detection of fake opinions on online products using Decision Tree and Information Gain” IEEE Xplore ISBN: 978-1-5386- 7808-4 2019. [5]. Syed Mohammed Anas and Santoshi Kumari “Opinion Mining based Fake Product review Monitoring and Removal System” IEEE Xplore ISBN: 978-1- 7281-8501-9 2021.
- 16. THANK YOU