Closes SEACrowd#720 | Add Sentiment-Annotated Taglish Product and Ser…

…vice Reviews dataloader (SEACrowd#728)

* Add Sentiment-Annotated Taglish Product and Service Reviews dataloader

* Corrected license

seacrowd/sea_datasets/sentiment_taglish_product_review/sentiment_taglish_product_review.py

@@ -0,0 +1,133 @@
+# coding=utf-8
+# Copyright 2022 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.
+
+"""\
+This dataset contains 10,510 examples of product and service reviews in Taglish
+from Google Maps Reviews and Shopee Philippines. Reviews are manually labeled by
+three human annotators according to four sentiment classes: Positive, Negative, Neutral, and Mixed.
+"""
+import csv
+from pathlib import Path
+from typing import Dict, Generator, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+# no citation found for this dataset
+_CITATION = ""
+_DATASETNAME = "sentiment_taglish_product_review"
+_DESCRIPTION = """\
+Sentiment-Annotated Taglish Product and Service Reviews (SentiTaglish: Products and
+Services) is a gold standard, sentiment-annotated corpus for the Tagalog-English
+language pair. It contains 10,510 product and service reviews which were manually
+labeled by three human annotators according to four sentiment classes:
+Positive, Negative, Neutral, and Mixed.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/ccosme/SentiTaglishProductsAndServices"
+
+_LANGUAGES = ["tgl", "eng"]
+_LICENSE = Licenses.CC_BY_4_0.value
+_LOCAL = False
+_URLS = {
+    _DATASETNAME: "https://huggingface.co/datasets/ccosme/SentiTaglishProductsAndServices/resolve/main/SentiTaglish_ProductsAndServices.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "1.0.0"
+
+
+class SentimentTaglishProductReviewDataset(datasets.GeneratorBasedBuilder):
+    """A sentiment-annotated corpus comprised of product/service reviews
+    in Tagalog-English (Taglish)"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "text"
+    LABEL_CLASSES = [str(i) for i in range(1, 5)]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({"review": datasets.Value("string"), "sentiment": datasets.features.ClassLabel(names=self.LABEL_CLASSES)})
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(self.LABEL_CLASSES)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Generator[Tuple[int, Dict], None, None]:
+        """Yields examples as (key, example) tuples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file,
+                quotechar='"',
+                delimiter=",",
+                quoting=csv.QUOTE_ALL,
+                skipinitialspace=True,
+            )
+            # skip first row
+            next(csv_reader)
+            for id_, row in enumerate(csv_reader):
+                review, sentiment = row
+                if self.config.schema == "source":
+                    yield id_, {"review": review, "sentiment": sentiment}
+                elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                    yield id_, {"id": id_, "text": review, "label": sentiment}