# Dataset Card for "twitter-coronavirus"

## Dataset Description

- **Homepage:** Kaggle Challenge
- **Repository:** https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification
- **Paper:** N.A.
- **Leaderboard:** N.A.
- **Point of Contact:** N.A.

### Dataset Summary

Perform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.
The names and usernames have been given codes to avoid any privacy concerns.

Columns:
1) Location
2) Tweet At
3) Original Tweet
4) Label
  - Extremely Negative
  - Negative
  - Neutral
  - Positive
  - Extremely Positive

### Languages

english 

### Citation Information

https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification


### Contributions

Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset.
task_categories:text-classification task_ids:sentiment-classification task_ids:sentiment-analysis size_categories:10K<n<100K source_datasets:original language:en license:unknown region:us
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TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-sentiment-analysis #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us 

# Dataset Card for "twitter-coronavirus"

## Dataset Description

- Homepage: Kaggle Challenge
- Repository: URL
- Paper: N.A.
- Leaderboard: N.A.
- Point of Contact: N.A.

### Dataset Summary

Perform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.
The names and usernames have been given codes to avoid any privacy concerns.

Columns:
1) Location
2) Tweet At
3) Original Tweet
4) Label
  - Extremely Negative
  - Negative
  - Neutral
  - Positive
  - Extremely Positive

### Languages

english 



URL


### Contributions

Thanks to @davidberenstein1957 for adding this dataset.
# Dataset Card for "twitter-coronavirus" ## Dataset Description

- Homepage: Kaggle Challenge
- Repository: URL
- Paper: N.A.
- Leaderboard: N.A.
- Point of Contact: N.A. ### Dataset Summary

Perform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.
The names and usernames have been given codes to avoid any privacy concerns.

Columns:
1) Location
2) Tweet At
3) Original Tweet
4) Label
  - Extremely Negative
  - Negative
  - Neutral
  - Positive
  - Extremely Positive ### Languages

english 



URL ### Contributions

Thanks to @davidberenstein1957 for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-sentiment-analysis #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us 
 # Dataset Card for "twitter-coronavirus" ## Dataset Description

- Homepage: Kaggle Challenge
- Repository: URL
- Paper: N.A.
- Leaderboard: N.A.
- Point of Contact: N.A. ### Dataset Summary

Perform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.
The names and usernames have been given codes to avoid any privacy concerns.

Columns:
1) Location
2) Tweet At
3) Original Tweet
4) Label
  - Extremely Negative
  - Negative
  - Neutral
  - Positive
  - Extremely Positive ### Languages

english 



URL ### Contributions

Thanks to @davidberenstein1957 for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-sentiment-analysis #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us 
# Dataset Card for "twitter-coronavirus"## Dataset Description

- Homepage: Kaggle Challenge
- Repository: URL
- Paper: N.A.
- Leaderboard: N.A.
- Point of Contact: N.A.### Dataset Summary

Perform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.
The names and usernames have been given codes to avoid any privacy concerns.

Columns:
1) Location
2) Tweet At
3) Original Tweet
4) Label
  - Extremely Negative
  - Negative
  - Neutral
  - Positive
  - Extremely Positive### Languages

english 



URL### Contributions

Thanks to @davidberenstein1957 for adding this dataset.