The goal of this project is to create a model to accurately classify a tweet about the pandemic as Positive, Negative, Neutral, Extremely Positive, or Extremely Negative. This project explores the use of common NLP techniques (text pre-processing, TF-IDF Vectorizer with basic classification models, neural networks with learned embeddings). The project is split up into four main sections each with its own tab on the website. It begins with EDA of the dataset to get a better understanding of the data and pre-processing of the tweets to prepare them for machine learning models. Then the actual modeling is done. The following models are used with the TF-IDF Vectorizer: Logistic Regression, Random Forest, XGBoost, Naive Bayes. The following architectures are used for neural networks with learned embeddings: ANN, LSTM. Next, the results of each model are provided along with some additional details on model development. Finally, the overall conclusions of the project are given.