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🤘 ReviewScrapper 🐍

Review-Scrapper

HitCount

📌 Introduction

ReviewScrapper is a web based application which is primarly used to scraps the reviews for a particualar products from Ecommerce websites and save the reviews in the csv format that can be further used for Data-Analysis. ReviewScrapper also allows user to limit the reviews based on the Data requirement and scraps the reviews from the multiple pages. :arrow_right: Live :beers:vikas-review-scrapper 🍻

💻 Tech Stack

🏃‍♀️ Local Installation Guide :

  1. Clone the repository by using Git Client:

      git clone https://github.com/palrohitg/reviewscrapper.git
    
  2. Make a virtual environment mandatory:

      * install pip, pipenv
      * Python -m venv virtual_env_name
      * make the virtual env and activate it 
    
  3. Install the Packages:

     pip install -r requirements.txt
    
  4. Configure the mongoDB not mandatory mlab :

      create Cluster, User Access and Network if need to make changes to db.
    
  5. Run the reviewscrapper and enjoy :

     python app.py
    
  6. Finally don't forget to put Star 😎

↘️ Outcome/Result:

☑️ Reduces the Customer time by 70% while spending time for checking review of the product.

☑️ Helps to Gather information how's customer feels about the specific product and provide better services from business point of view.

☑️ It can be used for offline Saler(Mobile) who want to upscale their business online. And Figuring out which product their put on their portal which increase the sales.

🎯 Scope of Improvement :

  • 🌼 UI can more Interactive.
  • 🌼 Scrap the reivews from multiple sites.
  • 🌼 More features can be added.
  • 🌼 Suggestion and feed-Back always welcome.

📱 Running Instance:

  • ➡️ Live 🍻vikas-review-scrapper 🍻
  • Note: Site maybe down I m working 🤞
  • Side Note: As the flipkart site has different design for different kind of product the review-scrapper not able to fetch the record. we have implement the desing related to electronic items.

📜 LICENSE

MIT

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Languages

  • HTML 65.4%
  • Python 32.5%
  • CSS 2.1%