metadata

license: mit
task_categories:
  - text-classification
language:
  - en
size_categories:
  - 1K<n<10K
tags:
  - binary-classification
  - tweets
  - natural-language-processing
pretty_name: Disaster vs Non-Disaster Tweets
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.csv
      - split: test
        path: test.csv

Disaster Tweets Dataset For Binary Classification

This dataset contains tweets classified as either disastrous (label 1) or not disastrous (label 0). It is designed to train and evaluate machine learning models for disaster-related tweet classification.

Files Included

train.csv: Contains 7,613 tweets with their respective labels.
test.csv: Contains 3,263 tweets without labels.

Columns

Each CSV file contains the following columns:

id – Unique identifier for each tweet.
keyword – A keyword extracted from the tweet (may be blank).
location – The geographical location where the tweet was posted (may be blank).
text – The actual content of the tweet.
(label in train.csv) – Classification of the tweet:
- 1 → Disastrous
- 0 → Not Disastrous

Example Rows

`train.csv` (Sample Data)

id	keyword	location	text	label
1			Just happened a terrible car crash	1
2			Heard about #earthquake in different cities, stay safe everyone!	1
3			Forest fire spotted at the park. Geese are fleeing across the street!	1
10			No I don’t like cold weather!	0
52	ablaze	Philadelphia	Crying out for more! Set me ablaze	0

`test.csv` (Sample Data)

id	keyword	location	text
11			Typhoon Soudelor kills 28 in China and Taiwan
46	ablaze	London	Birmingham Wholesale Market is ablaze! Fire breaks out at Birmingham's Wholesale Market
51	ablaze	NIGERIA	Toke Makinwa’s marriage crisis sets Nigerian Twitter ablaze…

Contributing

If you would like to improve or expand the dataset, feel free to submit suggestions or contributions. Feedback is always welcome!