This dataset consists of Covid-19 related tweets posted by users coming from six English-speaking countries: Australia, Canada, Ireland, New Zealand, the United Kingdom, and the United States. A total of 6 columns were provided, but only the tweet text and country were used to train the models.
Dataset was extend to provide better results by replacing emojis with their respective word and expanding the shortened urls to the original link in order to extract relevant words.
| text | country |
|---|---|
| Remember the #WuhanCoronaVirus? The pandemic w... | us |
| While we hit 150,000 in #COVID19 deaths, the P... | new_zealand |
| 🇺🇸 Pandémie de #coronavirus: 30 pasteurs améri... | us |
Within the Notebook is an extensive descriptive analysis using numerous NLP techniques in an attempt extract useful information from the dataset.
- Finding top ten hashtags
- Calculating statistics based on words, characters, and hashtags within the tweets
- Utilizing LDA to topics from the dataset
- Performing Non-negative Matrix Factorization for topic analysis
| model | accuracy |
|---|---|
| Ensemble Model | 51.3% |
| Logistic Regression | 45.2% |
| Linear SVC | 48.7% |
| Multinomial Naive Bayes | 49.4% |
Ensemble model combined results from a CNN built using keras and a Multinomial Naive Bayes model built using Scikit-learn.
