4Chan Sentiment Analysis Application

This application is a multi-container web application designed to scrape posts from the popular online platform, 4Chan, and perform sentiment analysis on the replies to these posts. The primary goal of the application is to predict and categorize the sentiments of the replies as either positive or negative.

Application Components

The application is composed of several interconnected containers, each serving a specific purpose:

1. Page Scraping Container: This container is responsible for scraping posts from 4Chan. It navigates through the site's pages, identifies posts, and extracts the necessary data.

2. UI Container: This container manages the user interface of the application. It presents the scraped data and sentiment analysis results in a user-friendly and intuitive manner.

3. Database Container: This container handles data storage and retrieval. It stores the scraped posts and the results of the sentiment analysis for future reference and analysis.

4. Sentiment Model Container: This container houses the sentiment analysis model. It takes the scraped posts as input and predicts the sentiment of the replies, categorizing them as either positive or negative.

5. FastAPI Container: This is the main container that handles communication between all other containers. It uses the FastAPI framework to ensure efficient and reliable communication.

Application Diagram

This is the application diagram that shows the architecture of the application and how the different containers interact with each other.

Application Interface

Initial UI

This is how the user interface looks like when the application is started. It's clean and ready for the user to interact with.

Wordcloud from Scraped Text

After scraping the posts, a wordcloud is generated to visually represent the most frequently used words in the scraped threads.

Sentiment Barplot

This barplot shows the sentiment analysis results for the entire database. It categorizes the sentiments as either positive or negative and displays the count of each.

Local Deployment and Running Instructions

This application is containerized using Docker, which makes it easy to deploy and run locally. Here are the steps to do so:

1. Prerequisites: Ensure that you have Docker and Docker Compose installed on your system. If not, you can download them from the Docker official website.

2. Clone the Repository: Clone this repository to your local machine using the following command in your terminal:

   git clone github.com/Chajf/OCP_Project
   

3. Navigate to the Project Directory: Change your current directory to the project's root directory with:

   cd OCP_Project
   

4. Build the Docker Images: Build the Docker images for each container using Docker Compose with the following command:

   docker-compose build
   

5. Run the Application: Finally, you can run the application with:

   docker-compose up
   

Conclusion

By integrating these containers, the application provides a comprehensive solution for analyzing sentiments on 4Chan. It not only identifies and categorizes sentiments but also presents the data in a way that can be easily understood and utilized. This makes it a valuable tool for understanding public opinion trends on 4Chan.