README.md

---
task_categories:
- text-classification
language:
- en
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype: int64
  - name: sa
    dtype: int64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 141506555
    num_examples: 1613790
  - name: test
    num_bytes: 39317936
    num_examples: 448276
  - name: dev
    num_bytes: 15759601
    num_examples: 179310
  download_size: 108306225
  dataset_size: 196584092
---

The Moji dataset (Blodgett et al., 2016) (http://slanglab.cs.umass.edu/TwitterAAE/) contains tweets used for sentiment analysis (either positive or negative sentiment), with additional information on the type of English used in the tweets which is a sensitive attribute considered in fairness-aware approaches (African-American English (AAE) or Standard-American English (SAE)).

The type of language is determined thanks to a supervised model. Only the data 
where the sensitive attribute is predicted with a certainty rate above a given threshold are kept. 

Based on this principle we make available two versions of the Moji dataset, 
respectively with a threshold of 80% and of 90%. The dataset's distributions are presented below.


### Dataset with 80% threshold

|  | Positive sentiment | Negative Sentiment | Total |
|---|---|---|---|
AAE | 73 013 | 44 023 | 117 036 |
SAE | 1 471 427 | 652 913 | 2 124 340 |
Total | 1 544 440 | 696 936 | 2 241 376 |

To load this dataset, use the following code :
```python
dataset = load_dataset("LabHC/moji", revision='moji_conf_08')
```
or by default the version is the dataset with 80% threshold
```python
dataset = load_dataset("LabHC/moji")
```

### Dataset with 90% threshold

|  | Positive sentiment | Negative Sentiment | Total |
|---|---|---|---|
AAE | 30 827 | 18 409 | 49 236 |
SAE | 793 867 | 351 600 | 1 145 467 |
Total | 824 694 | 370 009 | 1 194 703 |

To load this dataset, use the following code :
```python
dataset = load_dataset("LabHC/moji", revision='moji_conf_09')
```

----
[Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://aclanthology.org/D16-1120) (Blodgett et al., EMNLP 2016)