iPhone 12 Review Analysis

Table of Contents

• Introduction
• Features
• Installation
• Usage
• API Endpoints
• Technologies Used

Introduction

This project is a comprehensive solution for scraping, analyzing, and serving iPhone 12 reviews from Amazon. It includes web scraping capabilities, sentiment analysis, and a Flask-based API for interacting with the collected data.

The main components of the project are:

1. Web scraping script to collect iPhone 12 reviews from Amazon
2. Sentiment analysis of the collected reviews
3. API endpoints for retrieving reviews and performing sentiment analysis
4. A simple frontend for interacting with the API

Features

• Web Scraping: Collects reviews, ratings, and other metadata from Amazon's iPhone 12 product page.
• Sentiment Analysis: Analyzes the sentiment of reviews using NLTK and TextBlob.
• Keyword Analysis: Identifies the most impactful positive and negative keywords in the reviews.
• RESTful API: Provides endpoints for retrieving reviews and performing sentiment analysis on new text.
• Database Storage: Stores collected reviews in an SQLite database for easy access and management.
• Simple Frontend: Includes a basic HTML interface for interacting with the API.

Installation

1. Clone the repository:

   git clone https://github.com/charans2702/Amazon-ReviewRetrival-and-SentimentAnalysis.git
   cd Amazon_Scrapper
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. Download NLTK data:

   python -c "import nltk; nltk.download('vader_lexicon')"
   

Usage

1. Run the web scraping script:

   python Amazon_Scrapper.py
   

   This will collect reviews and save them to the amazon_reviews.db SQLite database.

2. Perform sentiment analysis on the collected reviews:

   python sentiment_analysis.py
   

3. Start the Flask API:

   python app.py
   

   The API will be available at http://localhost:5000.

4. Open index.html in a web browser to use the frontend interface.

USER INTERFACE

API Endpoints

1. Sentiment Analysis

   • URL: /sentiment
   • Method: POST
   • Data Params: { "text": "Review text here" }
   • Success Response: { "sentiment": "positive" }

2. Review Retrieval

   • URL: /reviews
   • Method: GET
   • URL Params:
     • color (optional)
     • storage (optional)
     • rating (optional)
   • Success Response: Array of review objects

Technologies Used

• Python 3.x
• BeautifulSoup4 for web scraping
• NLTK and TextBlob for sentiment analysis
• Flask for API development
• SQLite for database storage
• Pandas for data manipulation
• Scikit-learn for TF-IDF vectorization