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--- |
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task_categories: |
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- text-classification |
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language: |
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- en |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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- name: label |
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dtype: int64 |
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- name: sa |
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dtype: int64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 141506555 |
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num_examples: 1613790 |
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- name: test |
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num_bytes: 39317936 |
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num_examples: 448276 |
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- name: dev |
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num_bytes: 15759601 |
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num_examples: 179310 |
|
download_size: 108306225 |
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dataset_size: 196584092 |
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--- |
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|
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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)). |
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The type of language is determined thanks to a supervised model. Only the data |
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where the sensitive attribute is predicted with a certainty rate above a given threshold are kept. |
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Based on this principle we make available two versions of the Moji dataset, |
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respectively with a threshold of 80% and of 90%. The dataset's distributions are presented below. |
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### Dataset with 80% threshold |
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| | Positive sentiment | Negative Sentiment | Total | |
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|---|---|---|---| |
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AAE | 73 013 | 44 023 | 117 036 | |
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SAE | 1 471 427 | 652 913 | 2 124 340 | |
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Total | 1 544 440 | 696 936 | 2 241 376 | |
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To load this dataset, use the following code : |
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```python |
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dataset = load_dataset("LabHC/moji", revision='moji_conf_08') |
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``` |
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or by default the version is the dataset with 80% threshold |
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```python |
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dataset = load_dataset("LabHC/moji") |
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``` |
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### Dataset with 90% threshold |
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| | Positive sentiment | Negative Sentiment | Total | |
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|---|---|---|---| |
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AAE | 30 827 | 18 409 | 49 236 | |
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SAE | 793 867 | 351 600 | 1 145 467 | |
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Total | 824 694 | 370 009 | 1 194 703 | |
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To load this dataset, use the following code : |
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```python |
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dataset = load_dataset("LabHC/moji", revision='moji_conf_09') |
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``` |
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---- |
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[Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://aclanthology.org/D16-1120) (Blodgett et al., EMNLP 2016) |
