Airflow DAG for sentiment analysis on GCP

Airflow DAG that performs sentiment analysis on API-fetched news articles. The DAG is designed to be deployed on GCP Composer and run at a daily interval. During the DAG Run the processes below are serially executed:

1. A table is idempotently created on Google BigQuery. Each row represents a news article, with the table's columns being title, creator, description, country, category, description_sentiment, topic and retrievaldate.
2. News articles are fetched using the newsdata.io API client. The topic and volume of the articles fetched are configurable via the YAML file.
3. The articles' descriptions are processed with standard NLP methods and their sentiment is calculated with the Vader NLTK sub-module. Sentiment scores range from 0 (negative sentiment) to 1 (positive sentiment).
4. The results of step 3 are stored in the BigQuery table created in step 1.