# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """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_sentiment_url, translated_sentiment_url, translated_emotion_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_sentiment_url = manual_sentiment_url self.translated_sentiment_url = translated_sentiment_url self.translated_emotion_url = translated_emotion_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", 'machine': 'machine', 'human': 'human', 'emotion_intensity_score': 'emotion_intensity_score'}, label_classes=['positive', 'negative', 'surprise', 'fear', 'anticipation', 'anger', 'joy', 'trust', 'disgust', 'sadness'], label_column="label", manual_sentiment_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/lexicons/manual-sentiment/huggingface/{lang}.csv", translated_sentiment_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/lexicons/translated-sentiment/huggingface/{lang}.csv", translated_emotion_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/lexicons/translated-emotion/huggingface/{lang}.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_sentiment_path = dl_manager.download_and_extract(self.config.manual_sentiment_url) translated_sentiment_path = dl_manager.download_and_extract(self.config.translated_sentiment_url) translated_emotion_path = dl_manager.download_and_extract(self.config.translated_emotion_url) return [ datasets.SplitGenerator(name='manual-sentiment', gen_kwargs={"filepath": manual_sentiment_path}), datasets.SplitGenerator(name='translated-sentiment', gen_kwargs={"filepath": translated_sentiment_path}), datasets.SplitGenerator(name='translated-emotion', gen_kwargs={"filepath": translated_emotion_path}) ] def _generate_examples(self, filepath): df = pd.read_csv(filepath) print('-'*100) print(df.head()) print('-'*100) if self.config.name == 'translated-sentiment': for id_, row in df.iterrows(): word = row["word"] machine = row['machine'] human = row['human'] label = row["label"] yield id_, {"word": word, 'machine_translation': machine, 'human_translation': human, "label": label} elif self.config.name == 'manual-sentiment': for id_, row in df.iterrows(): word = row["word"] label = row["label"] yield id_, {"word": word, "label": label} else: for id_, row in df.iterrows(): word = row["word"] machine = row['machine'] human = row['human'] label = row["label"] emotion_intensity_score = row['emotion_intensity_score'] yield id_, {"word": word, 'machine_translation': machine, 'human_translation': human, "label": label, 'emotion_intensity_score': emotion_intensity_score}