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Introduction

With the rise of online marketplaces, the importance of customer reviews has increased significantly. Reviews play a crucial role in helping consumers make informed purchasing decisions. However, with the increase in fake reviews, the credibility of these online reviews has come under scrutiny. Fake reviews are created with the intention of deceiving consumers and manipulating their purchasing behavior. Detecting and filtering out fake reviews is essential to maintain the trustworthiness of online marketplaces.

Natural Language Processing (NLP) techniques have been widely used in various applications, including fake review detection. NLP involves the use of computational techniques to analyze and understand human language. By applying NLP techniques, researchers and practitioners can develop algorithms to automatically detect fake reviews with high accuracy.

This thesis aims to investigate fake review detection for online marketplaces using natural language processing. The study will explore various NLP techniques and apply them to detect fake reviews in online platforms. By developing effective fake review detection algorithms, this research aims to contribute to the integrity and reliability of online reviews.

Table of Contents

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to Fake Review Detection
2.2 Importance of Online Reviews
2.3 Types of Fake Reviews
2.4 Existing Methods for Fake Review Detection
2.5 NLP Techniques for Fake Review Detection
2.6 Evaluation Metrics for Fake Review Detection
2.7 Challenges in Fake Review Detection
2.8 Legal and Ethical Implications of Fake Review Detection
2.9 Future Trends in Fake Review Detection
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Introduction to Research Methodology
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Extraction
3.5 Model Development
3.6 Evaluation Method
3.7 Performance Metrics
3.8 Validation Techniques
3.9 Experimental Setup
3.10 Summary of Research Methodology

Chapter 4: Discussion of Findings
4.1 Introduction to Discussion
4.2 Analysis of Experimental Results
4.3 Comparison of Different NLP Techniques
4.4 Interpretation of Model Performance
4.5 Limitations of the Study
4.6 Implications for Online Marketplaces
4.7 Future Research Directions
4.8 Conclusion of Findings

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Practitioners
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview

Fake reviews pose a significant challenge in online marketplaces, affecting consumers’ trust and purchase decisions. This thesis focuses on utilizing natural language processing techniques to detect fake reviews and maintain the credibility of online platforms. The study includes a comprehensive review of existing literature on fake review detection, an exploration of NLP techniques, a detailed research methodology, a discussion of findings, and a conclusion summarizing the project’s key contributions and recommendations for future research. This research aims to provide valuable insights into fake review detection and contribute to enhancing the authenticity of online reviews.

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