| --- |
| task_categories: |
| - text-classification |
| language: |
| - en |
| dataset_info: |
| features: |
| - name: text |
| dtype: string |
| - name: label |
| dtype: int64 |
| - name: sa |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 95774165 |
| num_examples: 1194703 |
| download_size: 48721646 |
| dataset_size: 95774165 |
| --- |
| |
| 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 | |
|
|
|
|
| ### 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 | |
|
|
| ---- |
| [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://aclanthology.org/D16-1120) (Blodgett et al., EMNLP 2016) |
