Python Project With Source Code Ecommerce Fake Product Reviews Monitor

Python Project With Source Code Ecommerce Fake Product Reviews Monitor 

Ecommerce Fake Product Reviews Monitor and Deletion System using Python, you can follow this structured approach. This project will involve web scraping, sentiment analysis, user authentication, and database management. Here’s how you can outline and implement it:

Project Outline

1. Web Scraping Module

   • Use requests and BeautifulSoup for scraping product reviews from ecommerce websites.
   • Extract review content, ratings, timestamps, and other relevant metadata.

2. Sentiment Analysis

   • Utilize NLTK or TextBlob for sentiment analysis to classify reviews as positive, negative, or neutral based on text analysis.

3. Fake Review Detection

   • Implement algorithms to detect potential fake reviews:
     • Check for extreme ratings or sentiment polarity.
     • Analyze review patterns and user behaviors.
     • Identify suspicious patterns like similar content across multiple reviews.

4. User Authentication and Admin Panel

   • Develop user authentication using Flask-Login or Django.
   • Create an admin panel where authorized users can view flagged reviews and take actions.

5. Review Monitoring System

   • Set up a system to periodically check for new reviews.
   • Automatically classify new reviews and flag potential fakes for manual review.

6. Review Deletion

   • Allow authorized users to delete flagged reviews if confirmed as fake or violating platform policies.
   • Implement logging and confirmation mechanisms for review deletion.

7. Database Integration

   • Use SQLite or PostgreSQL for storing review data, user information, and admin actions.
   • Design a schema to support efficient querying and storage.

8. Reporting and Analytics

   • Generate reports on review trends, sentiment analysis results, and fake review detection rates.
   • Visualize data using matplotlib or Plotly for better insights.

Example Implementation Steps

Here’s how you can start implementing some key components of this project:

1. Setting Up the Environment

First, create a virtual environment and install necessary libraries:

$ mkdir ecommerce-review-monitor
$ cd ecommerce-review-monitor
$ python -m venv venv
$ source venv/bin/activate  # On Windows use `venv\Scripts\activate`
$ pip install requests beautifulsoup4 nltk flask sqlalchemy

2. Web Scraping Module

Create a script to scrape reviews from a sample ecommerce site using BeautifulSoup:

import requests
from bs4 import BeautifulSoup

def scrape_reviews(url):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
    }
    response = requests.get(url, headers=headers)
    
    if response.status_code == 200:
        soup = BeautifulSoup(response.content, 'html.parser')
        reviews = soup.find_all('div', class_='review')
        
        for review in reviews:
            review_text = review.find('p', class_='review-text').get_text()
            rating = review.find('span', class_='rating').get_text()
            # Extract other review metadata
            print(f"Rating: {rating}, Review Text: {review_text}")
    else:
        print("Failed to retrieve reviews.")

# Example usage
scrape_reviews('https://example.com/product/123/reviews')

3. Sentiment Analysis

Implement sentiment analysis using NLTK to classify reviews:

import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer

nltk.download('vader_lexicon')

def analyze_sentiment(review_text):
    sid = SentimentIntensityAnalyzer()
    sentiment_scores = sid.polarity_scores(review_text)
    
    if sentiment_scores['compound'] >= 0.05:
        return 'positive'
    elif sentiment_scores['compound'] <= -0.05:
        return 'negative'
    else:
        return 'neutral'

# Example usage
review_text = "This product is amazing!"
sentiment = analyze_sentiment(review_text)
print(f"Sentiment: {sentiment}")

4. User Authentication and Admin Panel

Set up user authentication and an admin panel using Flask:

from flask import Flask, render_template, redirect, url_for, request
from flask_sqlalchemy import SQLAlchemy
from flask_login import LoginManager, UserMixin, login_user, logout_user, login_required, current_user

app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db'
app.config['SECRET_KEY'] = 'your_secret_key'

db = SQLAlchemy(app)
login_manager = LoginManager(app)
login_manager.login_view = 'login'

class User(UserMixin, db.Model):
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(20), unique=True, nullable=False)
    password = db.Column(db.String(60), nullable=False)
    is_admin = db.Column(db.Boolean, default=False)

@login_manager.user_loader
def load_user(user_id):
    return User.query.get(int(user_id))

@app.route('/login', methods=['GET', 'POST'])
def login():
    if request.method == 'POST':
        username = request.form['username']
        password = request.form['password']
        user = User.query.filter_by(username=username).first()

        if user and user.password == password:
            login_user(user)
            return redirect(url_for('admin_panel'))
    
    return render_template('login.html')

@app.route('/admin_panel')
@login_required
def admin_panel():
    if not current_user.is_admin:
        return "You are not authorized to view this page."
    
    # Logic to display flagged reviews and actions
    return render_template('admin_panel.html')

@app.route('/logout')
@login_required
def logout():
    logout_user()
    return redirect(url_for('login'))

if __name__ == '__main__':
    app.run(debug=True)

5. Database Integration

Define database models and integrate SQLAlchemy for storing reviews and user data:

# Add this to the Flask app file (e.g., app.py)

# Example database model for reviews
class Review(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    product_id = db.Column(db.Integer, nullable=False)
    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
    content = db.Column(db.Text, nullable=False)
    rating = db.Column(db.Integer, nullable=False)
    timestamp = db.Column(db.DateTime, nullable=False, default=datetime.utcnow)

# Example database model for flagged reviews
class FlaggedReview(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    review_id = db.Column(db.Integer, db.ForeignKey('review.id'), nullable=False)
    reason = db.Column(db.Text, nullable=False)
    flagged_by = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)

# Example usage
review = Review(product_id=123, user_id=current_user.id, content="Good product", rating=5)
db.session.add(review)
db.session.commit()

Conclusion

This structured approach will help you develop a robust Ecommerce Fake Product Reviews Monitor and Deletion System using Python. Ensure to expand and refine each component based on your specific requirements and deployment environment. This project not only enhances your Python skills but also provides practical experience in web scraping, sentiment analysis, database management, and web application development