Project Title: Rapid Analyzer

This project is a full-stack application that uses React.js for the frontend, Flask and Python for sentiment analysis, and Node.js for data scraping. The application allows users to input a sentence and get a sentiment analysis result. It also allows users to input an App ID from the Google Play Store and get a detailed visualization of the app's ratings.

Technologies Used

React.js: A JavaScript library for building user interfaces.
Flask: A micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.
Python: A high-level, interpreted, interactive, and object-oriented scripting language.
SentimentIntensityAnalyzer from nltk.sentiment.vader: A tool used for sentiment analysis in Python.
Node.js: An open-source, cross-platform, back-end JavaScript runtime environment that runs on the V8 engine and executes JavaScript code outside a web browser.
google-play-scraper: A Node.js scraper module to get data from the Google Play Store.
D3.js: A JavaScript library for producing dynamic, interactive data visualizations in web browsers.
Vite: A build tool that aims to provide a faster and leaner development experience for modern web projects.

How to Run the Program

Clone the repository: Use the command git clone https://github.com/abirmehmed/Rapid-analyzer to clone the repository to your local machine.
Install dependencies: Navigate to the project directory and run npm install to install all the necessary dependencies.
Start the Vite server: Navigate to the project directory and run the following commands:
```
cd my-app
npm run dev
```
This will start the Vite server. You should see a message indicating that the server is running at http://localhost:5173/. You can Ctrl+Click this link to open it in your browser.
Start the Node.js server: Open a new terminal window and navigate to the Node.js server directory within the project. Run the following commands:
```
cd my-app/node
node app.js
```
This will start the Node.js server.
Start the Flask server: Open another new terminal window and activate your Python environment. Then, set the Flask app and start the Flask server with the following commands:
```
myenv\Scripts\activate
set FLASK_APP=app.py
flask run --port 5373
```
This will start the Flask server.
Open the application: You should now be able to interact with the application in your web browser at http://localhost:5173/.

Please note that you need to have all three servers (Vite, Node.js, and Flask) running simultaneously for the application to work correctly. Each server should be started in a separate terminal window.

Project Structure

The project is divided into two main parts: the sentiment analysis and the Google Play Store data visualization.

Sentiment Analysis

The sentiment analysis part of the application allows users to input a sentence and get a sentiment analysis result. The sentiment can be "positive", "negative", or "neutral". The sentiment analysis is done using the SentimentIntensityAnalyzer from nltk.sentiment.vader in Python.

Google Play Store Data Visualization

The Google Play Store data visualization part of the application allows users to input an App ID from the Google Play Store and get a detailed visualization of the app's ratings. The data is scraped from the Google Play Store using the google-play-scraper module in Node.js. The data visualization is done using D3.js.

Conclusion

This project demonstrates the use of various technologies to create a full-stack application. It showcases the use of React.js for building the frontend, Flask and Python for sentiment analysis, Node.js for data scraping, and D3.js for data visualization. The application provides a user-friendly interface for users to get sentiment analysis results and visualize Google Play Store data.