IMDB Movie review Scrapping

Scrapping the movie review ✏️ using python programming language💻.

🔍Welcome to the IMDb Movie Review Scraper project! 🌟 This Python script is designed to scrape movie reviews from IMDb, providing valuable data for analysis and research purposes. The IMDb Movie Review Scraping project aims to gather a new dataset by automatically extracting movie reviews from IMDb. This dataset will support various natural language processing tasks, including sentiment analysis and recommendation systems. Using web scraping techniques, such as Beautiful Soup, movie reviews are collected, preprocessed, and structured into a CSV format suitable for analysis, including Support Vector Machine classification. 📈

Features

Semi-supervised-sequence-learning-Project : replication process is done over here and for further analysis creation of new data is required.

1. Scraping Movie Reviews 🕵️‍♂️

• Movie_review_imdb_scrapping.ipynb - The script fetches user reviews from IMDb, providing access to a diverse range of opinions and feedback for different movies. It utilizes BeautifulSoup, a powerful Python library for web scraping, to extract data from IMDb's web pages efficiently and accurately. 🎥🔎

2. Customizable Scraper 🛠️

• rename_files.ipynb - Users can customize the scraper to target specific time periods, ratings, and other parameters, enabling focused data collection based on their requirements. This flexibility allows researchers, analysts, and enthusiasts to tailor the scraping process to their specific needs.

3. CSV Output 📁

• convert_texts_to_csv.ipynb - The scraped data is saved into a CSV file, allowing for easy import into data analysis software or further processing. The CSV format ensures compatibility with a wide range of tools and platforms, making it convenient to incorporate the scraped data into various workflows and projects. 💾💼

Getting Started

Dependencies

Make sure you have the following dependencies installed:

• Python 3.x
• BeautifulSoup (Install using pip install beautifulsoup4
• Pandas (Install using pip install pandas

Installation

1. Fork the Semi-supervised-sequence-learning-Project/ repository Link to `Semi-supervised-sequence-learning-Project' Follow these instructions on how to fork a repository

2. Clone the Repository to your local machine

   • using SSH:

     git clone git@github.com:your-username/Semi-supervised-sequence-learning-Project.git
     

   • Or using HTTPS:

     git clone https://github.com/your-username/Semi-supervised-sequence-learning-Project.git
     

3. Navigate to the project directory.

cd Semi-supervised-sequence-learning-Project

Usage

Starting the Streamlit app

1. Navigate to the Web_app directory

cd Web_app

2. Install requirements with pip

pip install -r requirements.txt

3. Run the Streamlit app

streamlit run streamlit_app.py

Uploading the CSV file

When prompted by the app, upload a CSV (comma separated value) file containing the reviews.

Demo Link

Streamlit app link: https://scrape-review-analysis.streamlit.app

Contribution

Contributions are welcome! If you have any suggestions for improvements or new features, please feel free to submit a pull request. Your contributions help make this project better for everyone.

Contribution

This project thanks all the contributors for having your valuable contribution to our project

Final Dataset

🔬Here is the Link to Final Dataset: Drive Link containing the scraped IMDb movie reviews. This dataset can be used for analysis, research, or any other purposes you require. 📦

Support

For any issues regarding the scraper, feel free to open an issue on GitHub. We'll be happy to assist you with any problems or inquiries you may have. 🛠️

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