|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis""" |
|
|
|
|
|
|
|
_HOMEPAGE = "https://github.com/hausanlp/NaijaSenti" |
|
|
|
_DESCRIPTION = """\ |
|
Naija-Stopwords is a part of the Naija-Senti project. It is a list of collected stopwords from the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá. |
|
""" |
|
|
|
|
|
_CITATION = """\ |
|
@inproceedings{muhammad-etal-2022-naijasenti, |
|
title = "{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual Sentiment Analysis", |
|
author = "Muhammad, Shamsuddeen Hassan and |
|
Adelani, David Ifeoluwa and |
|
Ruder, Sebastian and |
|
Ahmad, Ibrahim Sa{'}id and |
|
Abdulmumin, Idris and |
|
Bello, Bello Shehu and |
|
Choudhury, Monojit and |
|
Emezue, Chris Chinenye and |
|
Abdullahi, Saheed Salahudeen and |
|
Aremu, Anuoluwapo and |
|
Jorge, Al{\'\i}pio and |
|
Brazdil, Pavel", |
|
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", |
|
month = jun, |
|
year = "2022", |
|
address = "Marseille, France", |
|
publisher = "European Language Resources Association", |
|
url = "https://aclanthology.org/2022.lrec-1.63", |
|
pages = "590--602", |
|
} |
|
""" |
|
|
|
|
|
import textwrap |
|
import pandas as pd |
|
|
|
import datasets |
|
|
|
LANGUAGES = ['hausa', 'igbo', 'yoruba'] |
|
|
|
class NaijaLexiconsConfig(datasets.BuilderConfig): |
|
"""BuilderConfig for NaijaLexicons""" |
|
|
|
def __init__( |
|
self, |
|
text_features, |
|
label_column, |
|
label_classes, |
|
manual_url, |
|
translated_url, |
|
citation, |
|
**kwargs, |
|
): |
|
"""BuilderConfig for NaijaLexicons. |
|
|
|
Args: |
|
text_features: `dict[string]`, map from the name of the feature |
|
dict for each text field to the name of the column in the txt/csv/tsv file |
|
label_column: `string`, name of the column in the txt/csv/tsv file corresponding |
|
to the label |
|
label_classes: `list[string]`, the list of classes if the label is categorical |
|
train_url: `string`, url to train file from |
|
valid_url: `string`, url to valid file from |
|
test_url: `string`, url to test file from |
|
citation: `string`, citation for the data set |
|
**kwargs: keyword arguments forwarded to super. |
|
""" |
|
super(NaijaLexiconsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
|
self.text_features = text_features |
|
self.label_column = label_column |
|
self.label_classes = label_classes |
|
self.manual_url = manual_url |
|
self.translated_url = translated_url |
|
self.citation = citation |
|
|
|
|
|
class NaijaLexicons(datasets.GeneratorBasedBuilder): |
|
"""NaijaLexicons benchmark""" |
|
|
|
BUILDER_CONFIGS = [] |
|
|
|
for lang in LANGUAGES: |
|
BUILDER_CONFIGS.append( |
|
NaijaLexiconsConfig( |
|
name=lang, |
|
description=textwrap.dedent( |
|
f"""{_DESCRIPTION}""" |
|
), |
|
text_features={"word": "word"}, |
|
label_classes=["POSITIVE", "NEGATIVE"], |
|
label_column="label", |
|
manual_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/sentiment-lexicons/manual/{lang}/mixed.csv", |
|
translated_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/sentiment-lexicons/translated/{lang}/mixed.csv", |
|
citation=textwrap.dedent( |
|
f"""{_CITATION}""" |
|
), |
|
), |
|
) |
|
|
|
def _info(self): |
|
features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features} |
|
features["label"] = datasets.features.ClassLabel(names=self.config.label_classes) |
|
|
|
return datasets.DatasetInfo( |
|
description=self.config.description, |
|
features=datasets.Features(features), |
|
citation=self.config.citation, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
manual_path = dl_manager.download_and_extract(self.config.manual_url) |
|
translated_path = dl_manager.download_and_extract(self.config.translated_url) |
|
|
|
return [ |
|
datasets.SplitGenerator(name='manual', gen_kwargs={"filepath": manual_path}), |
|
datasets.SplitGenerator(name='translated', gen_kwargs={"filepath": translated_path}) |
|
] |
|
|
|
def _generate_examples(self, filepath): |
|
df = pd.read_csv(filepath) |
|
|
|
print('-'*100) |
|
print(df.head()) |
|
print('-'*100) |
|
|
|
for id_, row in df.iterrows(): |
|
word = row["word"] |
|
label = row["label"] |
|
|
|
yield id_, {"word": word, "label": label} |