How lexian-6/sentiment-analysis-towards-covid-19-on-twitter works
This repository is a research project for estimating sentiment in tweets about COVID-19. Its visible workflow ends at reviewing results in Main.ipynb; the tree also identifies four separate model notebooks (SRN, LSTM, DistilBERT, and XLNet) and several CSV corpus variants. The README does not document which files each notebook reads or how the model notebooks relate to Main.ipynb, and no source excerpts are available, so those connections are left unspecified.
Main entry points: Main.ipynb Research notebook
Components 9
Tweet data
- COVIDSenti corpus
COVIDSenti.csv - Corpus variants
Re-COVIDSenti.csv
Research results
- Research notebook
Main.ipynb - Sentiment results
Sentiment models
- SRN model
Model1: SRN.ipynb - LSTM model
Model2: LSTM.ipynb - DistilBERT model
Model3_distilBert.ipynb - XLNet model
Model4_XLnet.ipynb
Other components
- Researcher
Connections 2
- Researcher → Research notebook: reviews
- Research notebook → Sentiment results: presents