Sooryak12 / hruthiks-fake-review-detection-nlp-project

Amazon fake review detection using SVM model and bag of words concept

Fake Review Detection NLP Project

This project focuses on building a Natural Language Processing (NLP) model for detecting fake reviews. The goal is to use machine learning techniques to identify deceptive or fraudulent reviews among genuine ones.

Table of Contents

1. Introduction
2. Requirements
3. Data Preprocessing
   • Loading Libraries
   • Loading and Preprocessing the Dataset
4. Feature Engineering
   • Text Preprocessing
   • Feature Extraction using TF-IDF
   • Adding Verified Purchase as a Feature
5. Model Building
   • Support Vector Machine (SVM) Classifier
   • Model Training and Evaluation
6. Model Persistence
   • Saving the Trained Model
7. Conclusion
8. Future Improvements
9. License

Introduction

Fake reviews can be detrimental to businesses and consumers alike. This project leverages NLP techniques to develop a fake review detection system. The project involves data preprocessing, feature engineering, model training, and evaluation.

Requirements

Before running the code, make sure you have the following dependencies installed:

• nlppreprocess
• nltk

Data Preprocessing

The project begins by loading the necessary libraries and loading the dataset from an external source. The data is preprocessed to handle missing values and format conversion.

Feature Engineering

Text preprocessing is a critical step in NLP. The project performs the following:

• Removing HTML tags
• Removing punctuations and numbers
• Expanding contractions
• Removing stopwords, lemmatization, etc.

Feature extraction is done using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization. The "Verified Purchase" column is also added as a feature.

Model Building

The Support Vector Machine (SVM) classifier is chosen for this task. The model is trained using the preprocessed data and evaluated for accuracy.

Model Persistence

The trained SVM model is saved to a file using the pickle library. This allows the model to be used later without retraining.

Conclusion

Fake review detection is crucial for maintaining the credibility of online reviews. This project demonstrates how NLP techniques and machine learning can be employed to build an effective fake review detection system.

Future Improvements

• Experiment with different classifiers and feature engineering techniques.
• Explore more advanced NLP models such as deep learning architectures.
• Deploy the model as a web service for real-time fake review detection.

License

This project is provided under the MIT License. Feel free to use, modify, and distribute it for your purposes.