NaijaSenti-Twitter.py

# coding=utf-8
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis"""



_HOMEPAGE = "https://github.com/hausanlp/NaijaSenti"

_DESCRIPTION = """\
NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets.
"""


_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 csv
import textwrap
import pandas as pd

import datasets

LANGUAGES = ['hau', 'ibo', 'yor', 'pcm']

class NaijaSentiConfig(datasets.BuilderConfig):
    """BuilderConfig for NaijaSenti"""

    def __init__(
        self,
        text_features,
        label_column,
        label_classes,
        train_url,
        valid_url,
        test_url,
        citation,
        **kwargs,
    ):
        """BuilderConfig for NaijaSenti.

        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(NaijaSentiConfig, 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.train_url = train_url
        self.valid_url = valid_url
        self.test_url = test_url
        self.citation = citation


class NaijaSenti(datasets.GeneratorBasedBuilder):
    """NaijaSenti benchmark"""

    BUILDER_CONFIGS = []

    for lang in LANGUAGES:
        BUILDER_CONFIGS.append(
            NaijaSentiConfig(
                name=lang,
                description=textwrap.dedent(
                    f"""\
                    {lang} dataset."""
                ),
                text_features={"tweet": "tweet"},
                label_classes=["positive", "neutral", "negative"],
                label_column="label",
                train_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/annotated_tweets/{lang}/train.tsv",
                valid_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/annotated_tweets/{lang}/dev.tsv",
                test_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/annotated_tweets/{lang}/test.tsv",
                citation=textwrap.dedent(
                    f"""\
                    {lang} 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."""
        train_path = dl_manager.download_and_extract(self.config.train_url)
        valid_path = dl_manager.download_and_extract(self.config.valid_url)
        test_path = dl_manager.download_and_extract(self.config.test_url)
        
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
        ]

    def _generate_examples(self, filepath):
        df = pd.read_csv(filepath, sep='\t')

        print('-'*100)
        print(df.head())
        print('-'*100)

        for id_, row in df.iterrows():
            tweet = row["tweet"]
            label = row["label"]

            yield id_, {"tweet": tweet, "label": label}