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Our project lets users search product reviews by name. We scrape Amazon, eBay, and AliExpress, analyze using NLTK, and return processed data including details, positive and negative comments. Built with Django, React.js, and MongoDB. Scrapping with BeautifulSoup, Selenium; NLTK's SentimentIntensityAnalyzer() for analysis.
IBIZZI-Khalid/Data-Harvest-Ecommerce-Data-Collector-analyzer-
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Project Name : Data Harvest Description : Data Harvest is a web-based application designed to collect, analyze, and visualize data from various e-commerce platforms such as Amazon, eBay, and AliExpress. The application allows users to search for products and retrieve relevant information such as product details, reviews, and pricing data. It leverages web scraping techniques to gather data in real-time, providing users with valuable insights to inform their purchasing decisions. With an intuitive user interface and powerful backend processing, Data Harvest simplifies the process of gathering and analyzing e-commerce data. Key Features : -Search for products across multiple e-commerce platforms. -Retrieve product details, reviews, and pricing information. -Real-time data collection using web scraping techniques. -Intuitive user interface for easy navigation and interaction. -Backend processing for data analysis and visualization. Getting Started: To get started with Data Harvest, simply clone the repository and modify the paths for example the ones included in the scrapping scripts (\backend\api\scrappers) Contributing We welcome contributions from the community! If you'd like to contribute to Data Harvest . _____________________________________________________________________ to run the back end part : backend> Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass backend> VirtualEnv\Scripts\activate (VirtualEnv) py manage.py runserver To run the front end part: frontend> npm run dev
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Our project lets users search product reviews by name. We scrape Amazon, eBay, and AliExpress, analyze using NLTK, and return processed data including details, positive and negative comments. Built with Django, React.js, and MongoDB. Scrapping with BeautifulSoup, Selenium; NLTK's SentimentIntensityAnalyzer() for analysis.
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