User & Company relationship visualizing application

Visualize user sentiments for popular companies every week or month.

Project architecture

Let's understand the services in brief.

API service - An api gateway that manages all key data calling functionalities and creates a single point access for other microservices.
Scraper - The tweets scrapping microservice, whose only purpose is to get tweets from Twitter and store them inside a database.

Since, the scraper requirement is only once or twice a month, there is no need for a streaming service. That is the reason why a database is considered more easy and simple than any other real-time tool like Kafka.
Classification - The classification service uses Hugging face model for sentiment prediction of the tweets. Then the percentage of the different sentiments are calculated and store inside a database.
Frontend - The sentiments, stored on the database, are fetched by the frontend service and displayed to the users in the form of charts.

There are a few things that are needed to be done before starting the containers.

Since we are using firebase, make a firebase project and create a realtime database.
Then download the <key>.json file and save it inside the api/ directory.
Add the companies name to the scraper/config/config.yml file.
Run the jupyter notebook to download the model and save them to models directory inside the classification/ service.

Clone the repository
```
$ git clone <url>
```
Get inside the cloned directory
```
$ cd <directory>
```
Run the jupyter notebook to download the Hugging face model
Add companies name to the scraper/config/config.yml file.
Run docker-compose.yml to start the project
```
$ docker compose up --build
```