COVID_sentiment_project

Group Members

• Mariia Shubina
• Sijia Han
• Jiajing Li

Description

In this project, we have utilized CNN + BiLSTM, BERTweet and Fine-tuned BERTweet three models to predict the sentiment of tweets related to masks and vaccines.

All three models have achieved over 60% accuracy on the test sets. The BERTweet model outperforms the CNN+BiLSTM model and the fine-tuned BERTweet on both the SemEval 2017 test set and the vaccine test set, with accuracy and F-1 scores of 71.9%, 72% and 78.2%, 79.2%, respectively. However, the fine-tuned BERTweet has higher accuracy score and F-1 socre, 72.2% and 69.1%, among the three models on the mask test set.

Team Contract and Project Proposal

• Team Contract
• Project Proposal

Milestone 1

• Data Analysis Code
• CNN Model
• Bertweet Model

Milestone 2

• Bertweet Model
• CNN Model
• Twitter API
• Progress Report

Milestone 3

• Twitter Test Corpus
• Twitter Training Corpus
• Twitter Corpus Final Predictions
• CNN + BILSTM model
• BERTweet Model

Presentation

• Presentation Slides

Final Report

• Final Report