GitHub - masaki9/COVID-19-Tweets: COVID-19 Vaccine Tweets Sentiment Analysis

I developed classification models that classify COVID-19 vaccine-related tweets into three sentiment classes, positive, neutral, and negative, using scikit-learn. The models classified the sentiments of the tweets with 69% accuracy for Canada and 84% accuracy for the world.
I applied NLP techniques such as lemmatization to extract vaccine-related tweets using NLTK.
I extracted and analyzed n-grams related to vaccines from vaccine-related tweets.
Canada and worldwide data show more positive and neutral sentiments toward COVID-19 vaccines.

Name		Name	Last commit message	Last commit date
Latest commit History 27 Commits
data		data
data_hydrated		data_hydrated
images		images
.gitignore		.gitignore
README.md		README.md
eda.py		eda.py
etl_canada.py		etl_canada.py
etl_global.py		etl_global.py
hydrate.py		hydrate.py
model.py		model.py
utils.py		utils.py

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