IMDb Movie Review Analysis and Recommendation System 📽️

Analyzing movie reviews and providing recommendations using Python and Streamlit. 🎬💻. We have created two part in this WebApp 😎!!! :

We have created a movie review analysis part.
We have created a movie recommendation part.

For new data generation for sentiment analysis and recommendation system, we have written different Python script to fetch📊 data from IMDb, analyze sentiments, and provide movie recommendations, all converted into an interactive web app using Streamlit. 🌐📈

Features

Scraping Movie Reviews: Collects user reviews from IMDb using BeautifulSoup.
Customizable Scraper: Collects Movie Description from IMDb using Selenium.
Sentiment Analysis: Uses Support Vector Machine (SVM) to classify reviews as positive or negative.
Recommendations: Recommends top movies based on content of previous movie watched by user .
CSV Output: Saves the scraped data into a CSV file for further analysis.

Installation

Install the dependencies:

pip install streamlit
pip install beautifulsoup4
pip install requests
pip install pandas
pip install scikit-learn
pip install selenium

Usage

For Sentiment Analysis Part

Run the scraping script to collect movie reviews and save them into a CSV file. Open and execute the Jupyter notebook:
```
jupyter notebook notebooks/movie_review_imdb_scrapping.ipynb
```
Navigate to the Web_app directory:
```
cd Web_app
```

For Content-Based Movie Recommendation Part

Run the scraping script to collect movie desciption and save them into a CSV file. Open and execute the Python Script.

Note: you have to download web chromedriver and add it's path in Scrapper.py where it's mentioned driver path.
```
python -u "Scrapper.py"   
```
Run the similarity_model generating script to find out similarity we have made a model which we will use in our webapp. Open and execute the Jupyter notebook

Note: you have to necessarily run this model.ipynb as this will download similarity.pkl which is the model we use in Streamlit Webapp
```
jupyter notebook notebooks/model.ipynb
```

For HomePage

Navigate to the Web_app directory:
```
cd Web_app
```
Run the Streamlit app:
```
streamlit run Home_Page.py
```

Home Page

Sentiment Analysis Part

Content-Based Movie Recommendation Part