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--- |
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annotations_creators: |
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- expert-generated |
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language_creators: |
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- expert-generated |
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language: |
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- af |
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- an |
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- ar |
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- az |
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- be |
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- bg |
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- bn |
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- br |
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- bs |
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- ca |
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- cs |
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- cy |
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- da |
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- de |
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- el |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fo |
|
- fr |
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- fy |
|
- ga |
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- gd |
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- gl |
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- gu |
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- he |
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- hi |
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- hr |
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- ht |
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- hu |
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- hy |
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- ia |
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- id |
|
- io |
|
- is |
|
- it |
|
- ja |
|
- ka |
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- km |
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- kn |
|
- ko |
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- ku |
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- ky |
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- la |
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- lb |
|
- lt |
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- lv |
|
- mk |
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- mr |
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- ms |
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- mt |
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- nl |
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- nn |
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- 'no' |
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- pl |
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- pt |
|
- rm |
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- ro |
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- ru |
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- sk |
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- sl |
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- sq |
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- sr |
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- sv |
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- sw |
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- ta |
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- te |
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- th |
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- tk |
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- tl |
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- tr |
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- uk |
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- ur |
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- uz |
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- vi |
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- vo |
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- wa |
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- yi |
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- zh |
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- zhw |
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license: |
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- gpl-3.0 |
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multilinguality: |
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- multilingual |
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size_categories: |
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- 1K<n<10K |
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- n<1K |
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source_datasets: |
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- original |
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task_categories: |
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- text-classification |
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task_ids: |
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- sentiment-classification |
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pretty_name: SentiWS |
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dataset_info: |
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- config_name: af |
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features: |
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- name: word |
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dtype: string |
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- name: sentiment |
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dtype: |
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class_label: |
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names: |
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'0': negative |
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'1': positive |
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splits: |
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- name: train |
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num_bytes: 45954 |
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num_examples: 2299 |
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download_size: 0 |
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dataset_size: 45954 |
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- config_name: an |
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features: |
|
- name: word |
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dtype: string |
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- name: sentiment |
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dtype: |
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class_label: |
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names: |
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'0': negative |
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'1': positive |
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splits: |
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- name: train |
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num_bytes: 1832 |
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num_examples: 97 |
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download_size: 0 |
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dataset_size: 1832 |
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- config_name: ar |
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features: |
|
- name: word |
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dtype: string |
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- name: sentiment |
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dtype: |
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class_label: |
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names: |
|
'0': negative |
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'1': positive |
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splits: |
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- name: train |
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num_bytes: 58707 |
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num_examples: 2794 |
|
download_size: 0 |
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dataset_size: 58707 |
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- config_name: az |
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features: |
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- name: word |
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dtype: string |
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- name: sentiment |
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dtype: |
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class_label: |
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names: |
|
'0': negative |
|
'1': positive |
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splits: |
|
- name: train |
|
num_bytes: 40044 |
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num_examples: 1979 |
|
download_size: 0 |
|
dataset_size: 40044 |
|
- config_name: be |
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features: |
|
- name: word |
|
dtype: string |
|
- name: sentiment |
|
dtype: |
|
class_label: |
|
names: |
|
'0': negative |
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'1': positive |
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splits: |
|
- name: train |
|
num_bytes: 41915 |
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num_examples: 1526 |
|
download_size: 0 |
|
dataset_size: 41915 |
|
- config_name: bg |
|
features: |
|
- name: word |
|
dtype: string |
|
- name: sentiment |
|
dtype: |
|
class_label: |
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names: |
|
'0': negative |
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'1': positive |
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splits: |
|
- name: train |
|
num_bytes: 78779 |
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num_examples: 2847 |
|
download_size: 0 |
|
dataset_size: 78779 |
|
- config_name: bn |
|
features: |
|
- name: word |
|
dtype: string |
|
- name: sentiment |
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dtype: |
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class_label: |
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names: |
|
'0': negative |
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'1': positive |
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splits: |
|
- name: train |
|
num_bytes: 70928 |
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num_examples: 2393 |
|
download_size: 0 |
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dataset_size: 70928 |
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- config_name: br |
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features: |
|
- name: word |
|
dtype: string |
|
- name: sentiment |
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dtype: |
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class_label: |
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names: |
|
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'1': positive |
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splits: |
|
- name: train |
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num_bytes: 3234 |
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num_examples: 184 |
|
download_size: 0 |
|
dataset_size: 3234 |
|
- config_name: bs |
|
features: |
|
- name: word |
|
dtype: string |
|
- name: sentiment |
|
dtype: |
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class_label: |
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names: |
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'1': positive |
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splits: |
|
- name: train |
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num_bytes: 39890 |
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num_examples: 2020 |
|
download_size: 0 |
|
dataset_size: 39890 |
|
- config_name: ca |
|
features: |
|
- name: word |
|
dtype: string |
|
- name: sentiment |
|
dtype: |
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class_label: |
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names: |
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splits: |
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- name: train |
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num_examples: 3204 |
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download_size: 0 |
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dataset_size: 64512 |
|
- config_name: cs |
|
features: |
|
- name: word |
|
dtype: string |
|
- name: sentiment |
|
dtype: |
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class_label: |
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names: |
|
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splits: |
|
- name: train |
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num_bytes: 53194 |
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num_examples: 2599 |
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download_size: 0 |
|
dataset_size: 53194 |
|
- config_name: cy |
|
features: |
|
- name: word |
|
dtype: string |
|
- name: sentiment |
|
dtype: |
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class_label: |
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names: |
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splits: |
|
- name: train |
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num_bytes: 31546 |
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num_examples: 1647 |
|
download_size: 0 |
|
dataset_size: 31546 |
|
- config_name: da |
|
features: |
|
- name: word |
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dtype: string |
|
- name: sentiment |
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dtype: |
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class_label: |
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names: |
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splits: |
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- name: train |
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num_bytes: 66756 |
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num_examples: 3340 |
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download_size: 0 |
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dataset_size: 66756 |
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- config_name: de |
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features: |
|
- name: word |
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dtype: string |
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- name: sentiment |
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dtype: |
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class_label: |
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splits: |
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num_bytes: 82223 |
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num_examples: 3974 |
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download_size: 0 |
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dataset_size: 82223 |
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- config_name: el |
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features: |
|
- name: word |
|
dtype: string |
|
- name: sentiment |
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dtype: |
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splits: |
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num_bytes: 76281 |
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num_examples: 2703 |
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download_size: 0 |
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dataset_size: 76281 |
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- config_name: eo |
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features: |
|
- name: word |
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dtype: string |
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- name: sentiment |
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dtype: |
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class_label: |
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splits: |
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num_bytes: 50271 |
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num_examples: 2604 |
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download_size: 0 |
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dataset_size: 50271 |
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- config_name: es |
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features: |
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- name: word |
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dtype: string |
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- name: sentiment |
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dtype: |
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class_label: |
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names: |
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splits: |
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num_bytes: 87157 |
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num_examples: 4275 |
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download_size: 0 |
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dataset_size: 87157 |
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- config_name: et |
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features: |
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- name: word |
|
dtype: string |
|
- name: sentiment |
|
dtype: |
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class_label: |
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names: |
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splits: |
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num_bytes: 41964 |
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num_examples: 2105 |
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download_size: 0 |
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dataset_size: 41964 |
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- config_name: eu |
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features: |
|
- name: word |
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dtype: string |
|
- name: sentiment |
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dtype: |
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names: |
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splits: |
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num_bytes: 39641 |
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num_examples: 1979 |
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download_size: 0 |
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dataset_size: 39641 |
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- config_name: fa |
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features: |
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dtype: string |
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num_examples: 2477 |
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download_size: 0 |
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dataset_size: 53399 |
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- config_name: fi |
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features: |
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dtype: string |
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- name: sentiment |
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- config_name: fo |
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num_examples: 123 |
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- config_name: fr |
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- config_name: fy |
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num_examples: 224 |
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- config_name: ga |
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num_examples: 1073 |
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download_size: 0 |
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dataset_size: 21209 |
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- config_name: gd |
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num_examples: 345 |
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download_size: 0 |
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dataset_size: 6441 |
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- config_name: gl |
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- config_name: gu |
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- config_name: he |
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- config_name: hi |
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- config_name: ka |
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- pt |
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- rm |
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- ro |
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- ru |
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- sk |
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- sl |
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- sq |
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- sr |
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- sv |
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- sw |
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- ta |
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- te |
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- th |
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- tk |
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- tl |
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- tr |
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- uk |
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- ur |
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- vo |
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- wa |
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- yi |
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- zh |
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- zhw |
|
--- |
|
|
|
# Dataset Card for SentiWS |
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
|
- [Annotations](#annotations) |
|
- [Personal and Sensitive Information](#personal-and-sensitive-information) |
|
- [Considerations for Using the Data](#considerations-for-using-the-data) |
|
- [Social Impact of Dataset](#social-impact-of-dataset) |
|
- [Discussion of Biases](#discussion-of-biases) |
|
- [Other Known Limitations](#other-known-limitations) |
|
- [Additional Information](#additional-information) |
|
- [Dataset Curators](#dataset-curators) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** https://sites.google.com/site/datascienceslab/projects/multilingualsentiment |
|
- **Repository:** https://www.kaggle.com/rtatman/sentiment-lexicons-for-81-languages |
|
- **Paper:** https://aclanthology.org/P14-2063/ |
|
- **Leaderboard:** [Needs More Information] |
|
- **Point of Contact:** [Needs More Information] |
|
|
|
### Dataset Summary |
|
|
|
This dataset add sentiment lexicons for 81 languages generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them |
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
Sentiment-Classification |
|
|
|
### Languages |
|
|
|
Afrikaans |
|
Aragonese |
|
Arabic |
|
Azerbaijani |
|
Belarusian |
|
Bulgarian |
|
Bengali |
|
Breton |
|
Bosnian |
|
Catalan; Valencian |
|
Czech |
|
Welsh |
|
Danish |
|
German |
|
Greek, Modern |
|
Esperanto |
|
Spanish; Castilian |
|
Estonian |
|
Basque |
|
Persian |
|
Finnish |
|
Faroese |
|
French |
|
Western Frisian |
|
Irish |
|
Scottish Gaelic; Gaelic |
|
Galician |
|
Gujarati |
|
Hebrew (modern) |
|
Hindi |
|
Croatian |
|
Haitian; Haitian Creole |
|
Hungarian |
|
Armenian |
|
Interlingua |
|
Indonesian |
|
Ido |
|
Icelandic |
|
Italian |
|
Japanese |
|
Georgian |
|
Khmer |
|
Kannada |
|
Korean |
|
Kurdish |
|
Kirghiz, Kyrgyz |
|
Latin |
|
Luxembourgish, Letzeburgesch |
|
Lithuanian |
|
Latvian |
|
Macedonian |
|
Marathi (Marāṭhī) |
|
Malay |
|
Maltese |
|
Dutch |
|
Norwegian Nynorsk |
|
Norwegian |
|
Polish |
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Portuguese |
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Romansh |
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Romanian, Moldavian, Moldovan |
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Russian |
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Slovak |
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Slovene |
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Albanian |
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Serbian |
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Swedish |
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Swahili |
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Tamil |
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Telugu |
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Thai |
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Turkmen |
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Tagalog |
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Turkish |
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Ukrainian |
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Urdu |
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Uzbek |
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Vietnamese |
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Volapük |
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Walloon |
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Yiddish |
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Chinese |
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Zhoa |
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## Dataset Structure |
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### Data Instances |
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``` |
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{ |
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"word":"die", |
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"sentiment": 0, #"negative" |
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} |
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``` |
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### Data Fields |
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- word: one word as a string, |
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- sentiment-score: the sentiment classification of the word as a string either negative (0) or positive (1) |
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### Data Splits |
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[Needs More Information] |
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## Dataset Creation |
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### Curation Rationale |
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[Needs More Information] |
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### Source Data |
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#### Initial Data Collection and Normalization |
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[Needs More Information] |
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#### Who are the source language producers? |
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[Needs More Information] |
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### Annotations |
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#### Annotation process |
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[Needs More Information] |
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#### Who are the annotators? |
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[Needs More Information] |
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### Personal and Sensitive Information |
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[Needs More Information] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[Needs More Information] |
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### Discussion of Biases |
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[Needs More Information] |
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### Other Known Limitations |
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[Needs More Information] |
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## Additional Information |
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### Dataset Curators |
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[Needs More Information] |
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### Licensing Information |
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GNU General Public License v3. |
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It is distributed here under the [GNU General Public License](http://www.gnu.org/licenses/gpl-3.0.html). |
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Note that this is the full GPL, which allows many free uses, but does not allow its incorporation into any type of distributed proprietary software, even in part or in translation. |
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For commercial applications please contact the dataset creators (see "Citation Information"). |
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### Citation Information |
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This dataset was collected by Yanqing Chen and Steven Skiena. If you use it in your work, please cite the following paper: |
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```bibtex |
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@inproceedings{chen-skiena-2014-building, |
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title = "Building Sentiment Lexicons for All Major Languages", |
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author = "Chen, Yanqing and |
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Skiena, Steven", |
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booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", |
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month = jun, |
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year = "2014", |
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address = "Baltimore, Maryland", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/P14-2063", |
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doi = "10.3115/v1/P14-2063", |
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pages = "383--389", |
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} |
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``` |
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### Contributions |
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Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset. |
