IMDB Movie review Scraping

Scraping the movie review ✏️ using python programming language💻.

For new data generation Semi-supervised-sequence-learning-Project we have written a python script to fetch📊, data from the 💻, imdb website and converted into txt files.

This project aims to replicate the Semi-supervised-sequence-learning-Project on a new dataset generated through scraping IMDb movie reviews. The generated data will be utilized for further analysis and exploration.

Introduction

Semi-supervised-sequence-learning-Project 💻 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.

• The following script includes the following.

• Movie_review_imdb_scrapping.ipynb - Script to scrape the data from imdb website

• rename_files.ipynb - Script to rename the scrapped text files as per the requirements

• convert_texts_to_csv.ipynb - Python script to make a CSV file from the txt files for SVM processing

• Movie_review_imdb_scrapping.ipynb - Script to scrape the data from IMDb website

• rename_files.ipynb - Script to rename the scraped text files as per the requirements

• convert_texts_to_csv.ipynb - Python script for converting the scraped text files into a CSV format suitable for SVM processing

Dependencies

Ensure Beautifulsoup is installed using pip install beautifulsoup4

Installation

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

2️⃣ Cloning the repository
Once you have set up your fork of the /Semi-supervised-sequence-learning-Project repository, you'll want to clone it to your local machine. This is so you can make and test all of your personal edits before adding it to the master version of /Semi-supervised-sequence-learning-Project.

Navigate to the location on your computer where you want to host your code. Once in the appropriate folder, run the following command to clone the repository to your local machine.

git clone [email protected]:your-username/sanjay-kv/Semi-supervised-sequence-learning-Project.git

Final Dataset

1️⃣ Here is the Link to Final Dataset: Drive Link