In this assignment, we have applied natural language processing inorder to understand the sentiment in the latest news articles featuring Bitcoin and Ethereum. We have also used fundamental NLP techniques to try and understand the other factors involved with the coin prices such as common words and phrases and organizations and entities mentioned in the articles.

In the Jupyter notebook we have performed the following tasks:

1. Sentiment Analysis - We used the newsapi to pull the latest news articles for Bitcoin and Ethereum and created a DataFrame of sentiment scores for each coin.
2. Natural Language Processing - We used NLTK and Python to tokenize text, find n-gram counts, and created word clouds for both coins.
3. Named Entity Recognition - We built a named entity recognition model for both coins and visualized the tags using SpaCy.

Further details and codes can be seen in the Jupyter notebook.

Thanks!