The crypto_sentiment notebook applies natural language processing to understand the sentiment in the latest news articles featuring Bitcoin and Ethereum. The code also applies 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.
Language: Python3, Pandas
Imports: os, pandas, dotenv, re, punctuation from string, matplotlib inline, SentimentIntensityAnalyzer from nltk.sentiment.vader, NewsApiClient from newsapi, word_tokenize and sent_tokenize from nltk.tokenize, stopwords from nltk.corpus, WordNetLemmatizer and PorterStemmer from nltk.stem, Counter from collections, and ngrams from nltk
External Resources: News API
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Example of wordcloud:
Example of NER visualization:
Drew Disbrow Marnell: dldmarnell@gmail.com
MIT License Copyright (c) 2021 Drew Disbrow Marnell