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Data Engineering project aimed to classify the severity of text primarily displayed by comments from Star Wars YouTube trailers.

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Star Wars YouTube Comments Sentiment Pipeline

Installation

1). Clone this repository by git clone https://github.com/tyeborg/mean-tweet-pipeline.git

2). Navigate to the pipeline directory by entering the following in the command line:

cd pipeline

3). Then, navigate to the airflow directory by entering the following in the command line:

cd airflow

4). Install the required packages to execute the pipeline:

pip install -r requirements.txt

5). Open the Docker Application and ensure that you don't have any other containers running using docker ps

6). On all operating systems, you need to run database migrations and create the first user account. To do this, run:

docker compose up airflow-init

7). After initialization is complete, enter the following to build the Docker container (running Airflow services):

docker compose up

8). Visit Apache Airflow webserver at: http://localhost:8080

9). Login into Airflow -- Login: airflow; Password: airflow

10). Navigate to the Admin tab and select Connections to establish a connection to the PostgreSQL database.

11). Create a connection with the following information:

  • Connection Id: postgre_sql
  • Connection Type: Postgres
  • Host: postgres
  • Schema: airflow
  • Login: airflow
  • Password: airflow
  • Port: 5432

12). Navigate to the Admin tab once more and select Variables to create a variable that enables access towards the established PostgreSQL database.

13). Create a variable with the following information:

  • Key: postgres_connection
  • Val:
{
    "host": "postgres",
    "port": "5432",
    "schema": "airflow",
    "login": "airflow",
    "password": "airflow"
}

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Data Engineering project aimed to classify the severity of text primarily displayed by comments from Star Wars YouTube trailers.

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