Create README.md

README.md

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+---
+task_categories:
+- text-classification
+language:
+- en
+---
+
+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)