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https://github.com/sarahm44/crypto-sentiment-analysis

Analysis of the sentiment of the latest news articles on Bitcoin and Ethereum using sentiment analysis, natural language processing and named entity recognition.
https://github.com/sarahm44/crypto-sentiment-analysis

bitcoin bitcoin-sentiment crypto-sentiment ethereum ethereum-sentiment fintech named-entity-recognition natural-language-processing newsapi sentiment-analysis

Last synced: 5 days ago
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Analysis of the sentiment of the latest news articles on Bitcoin and Ethereum using sentiment analysis, natural language processing and named entity recognition.

Host: GitHub
URL: https://github.com/sarahm44/crypto-sentiment-analysis
Owner: sarahm44
Created: 2022-05-15T09:12:00.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2022-08-10T03:02:45.000Z (over 2 years ago)
Last Synced: 2025-01-26T05:18:42.530Z (20 days ago)
Topics: bitcoin, bitcoin-sentiment, crypto-sentiment, ethereum, ethereum-sentiment, fintech, named-entity-recognition, natural-language-processing, newsapi, sentiment-analysis
Language: Jupyter Notebook
Homepage:
Size: 2.69 MB
Stars: 3
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        # Crypto Sentiment Analysis

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/sentimental.jpeg)

## Table of Contents

- [Overview](#overview)

- [Sentiment Analysis](#sentiment-analysis)

  * [Bitcoin Sentiment](#bitcoin-sentiment)

  * [Ethereum Sentiment](#ethereum-sentiment)

- [Natural Language Processing](#natural-language-processing)

  * [Tokenize](#tokenize)

  * [N-grams](#n-grams)

  * [Word Clouds](#word-clouds)

  * [Named Entity Recognition](#named-entity-recognition)

## Overview

In this repository 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.

I completed the following tasks:

1. Sentiment Analysis

2. Natural Language Processing

3. Named Entity Recognition

See this contained in this [Jupyter Lab notebook](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/crypto_sentiment_1.ipynb).

## Sentiment Analysis

I used the [newsapi](https://newsapi.org/) to pull the latest news articles for Bitcoin and Ethereum and created a DataFrame of sentiment scores for each coin.

### Bitcoin Sentiment

I created the Bitcoin sentiment scores dataframe:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/btc_df.png)

See Bitcoin sentiment below:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/btc_sentiment.png)

### Ethereum Sentiment

I created the Ethereum sentiment scores dataframe:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/eth_df.png)

See Ethereum sentiment as follows:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/eth_sentiment.png)

Some observations include that:

* Ethereum had the highest mean positive score.

* Ethereum had the highest mean compound score.

* Bitcoin had the highest max compound score.

## Natural Language Processing

In this section, I used NLTK and Python to tokenize text, find n-gram counts, and create word clouds for both coins. 

### Tokenize

I used NLTK and Python to tokenize the text for each coin. I completed the following:

1. Changed each word to lowercase.

2. Removed punctuation.

3. Removed stop words.

See relevant code below:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/tokenize.png)

I then added the "Tokens" column of the tokenized text to the dataframe:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/tokens_df.png)

### N-grams

Then I looked at the ngrams and word frequency for each coin.

I completed as follows:

1. Used NLTK to produce the ngrams for N = 2.

2. Listed the top 10 words for each coin.

See below the count for ngrams for N = 2:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/ngrams.png)

See below the code and results for the top 10 words for each coin:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/top10.png)

### Word Clouds

Finally, I generated word clouds for each coin to summarize the news for each coin.

See Bitcoin word cloud:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/btc_cloud.png)

See Ethereum word cloud:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/eth_cloud.png)

### Named Entity Recognition

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

See Bitcoin NER:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/btc_ner.png)

See Ethereum NER:

![](https://github.com/sarahm44/crypto-sentiment-analysis/blob/main/images/eth_ner.png)