Natural Language Programming Using Sentiment Analysis

This project applises NLP to understand the sentiment in the latest news articles featuring Bitcoin and Ethereum. Applied are 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.

Applications: Sentiment Analysis Natural Language Processing Named Entity Recognition

Files Starter Notebook contains python code

1 - Sentiment Analysis Using the newsapi to pull the latest news articles for Bitcoin and Ethereum and create a DataFrame of sentiment scores for each coin. Answers using descriptive statistics

2 - Natural Language Processing Application of NLTK and Python to tokenize text, find n-gram counts, and create word clouds for both coins.

N-grams

Word Clouds

3 - Named Entity Recognition Machine Learning Model for both coins and visualization of the tags using SpaCy.

Resources Vader Sentiment Analysis

Sentiment Analysis includes:

Determine the coin with Highest Mean Positive Score. Determine the coin with Highest Negative Score. Determine the coin with Highest Positive Score.

Natural Language Processing

Use NLTK to lower case words, remove punctuation and remove stopwords. Use NLTK to produce n-grams. List the top 10 words for each Coin. Generate a Word Cloud for each Coin summarizing the news for each Coin.

Named Entity Recognition

Build a Named Entity Recognition model for both coins - Bitcoin and Ethereum - and visualized the tags for each coin using SpaCy.