Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
Azerbaijani
Size:
100K - 1M
License:
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,18 @@
|
|
| 1 |
---
|
| 2 |
license: mit
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
license: mit
|
| 3 |
+
task_categories:
|
| 4 |
+
- text-classification
|
| 5 |
+
language:
|
| 6 |
+
- az
|
| 7 |
+
size_categories:
|
| 8 |
+
- 100K<n<1M
|
| 9 |
---
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
This dataset contains 150K (train + test) cleaned tweets in Azerbaijani. Tweets were collected in 2021, and filtered and cleaned by following these steps:
|
| 13 |
+
|
| 14 |
+
- Initial data were collected by using twint library. The tool is currently deprecated, cannot be used with new Twitter.
|
| 15 |
+
- On top of the already filtered data, I applied an additional filter to select Azerbaijani tweets with using fastText language identification model.
|
| 16 |
+
- Tweets were classified into 3 emotion categories: {positive: 1, negative: -1, neutral: 0} by using emojis as rule-based classifier.
|
| 17 |
+
- Tags, usernames, and emojis were later cleaned.
|
| 18 |
+
- Short tweets were filtered out.
|