Tales from the Crypto

by Arzu Isik Topbas

Background

I applied natural language processing to understand the sentiment in the latest news articles featuring Bitcoin and Ethereum. I also applied fundamental NLP techniques to better understand the other factors involved with the coin prices such as common words and phrases and organizations and entities mentioned in the articles.

1 - Sentiment Analysis

I used the newsapi to pull the latest news articles for Bitcoin and Ethereum and create a DataFrame of sentiment scores for each coin.

• Which coin had the highest mean positive score?

  • A: Bitcoin had the highest mean positive score with a mean of 0.102, compared to Ethereum's mean positive score of 0.042.

• Which coin had the highest negative score?

  • A: Bitcoin had the highest compound score with a max of 0.817, compared to Ethereum's max compound score of 0.636.

• Which coin had the highest positive score?

  • A: Bitcoin had the highest positive score with a max of 0.233, compared to Ethereum's max positive score of 0.155.

2 - Natural Language Processing

In this section, I used NLTK and Python to tokenize the text for each coin. I also looked at the ngrams and word frequency for each coin. Finally, I generated word clouds for each coin to summarize the news for each coin.

Bitcoin Word Cloud

Ethereum Word Cloud

3 - Named Entity Recognition

In this section, I built a named entity recognition model for both coins and visualize the tags using SpaCy.

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