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hoasa.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """
+@inproceedings{azhar2019multi,
+  title={Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting},
+  author={A. N. Azhar, M. L. Khodra, and A. P. Sutiono}
+  booktitle={Proceedings of the 2019 International Conference on Electrical Engineering and Informatics (ICEEI)},
+  pages={35--40},
+  year={2019}
+}
+"""
+
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_DATASETNAME = "hoasa"
+
+_DESCRIPTION = """
+HoASA: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, AiryRooms.
+The dataset covers ten different aspects of hotel quality. Similar to the CASA dataset, each review is labeled with a single sentiment label for each aspect.
+There are four possible sentiment classes for each sentiment label:
+positive, negative, neutral, and positive-negative.
+The positivenegative label is given to a review that contains multiple sentiments of the same aspect but for different objects (e.g., cleanliness of bed and toilet).
+"""
+
+_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
+
+_LICENSE = "CC-BY-SA 4.0"
+
+_URLS = {
+    "train": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/hoasa_absa-airy/train_preprocess.csv",
+    "validation": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/hoasa_absa-airy/valid_preprocess.csv",
+    "test": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/hoasa_absa-airy/test_preprocess.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.ASPECT_BASED_SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class HoASA(datasets.GeneratorBasedBuilder):
+    """HoASA is an aspect based sentiment analysis dataset"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="hoasa_source",
+            version=SOURCE_VERSION,
+            description="HoASA source schema",
+            schema="source",
+            subset_id="hoasa",
+        ),
+        NusantaraConfig(
+            name="hoasa_nusantara_text_multi",
+            version=NUSANTARA_VERSION,
+            description="HoASA Nusantara schema",
+            schema="nusantara_text_multi",
+            subset_id="hoasa",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "hoasa_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "index": datasets.Value("int64"),
+                    "review": datasets.Value("string"),
+                    "ac": datasets.Value("string"),
+                    "air_panas": datasets.Value("string"),
+                    "bau": datasets.Value("string"),
+                    "general": datasets.Value("string"),
+                    "kebersihan": datasets.Value("string"),
+                    "linen": datasets.Value("string"),
+                    "service": datasets.Value("string"),
+                    "sunrise_meal": datasets.Value("string"),
+                    "tv": datasets.Value("string"),
+                    "wifi": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "nusantara_text_multi":
+            features = schemas.text_multi_features(["pos", "neut", "neg", "neg_pos"])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        train_csv_path = Path(dl_manager.download_and_extract(_URLS["train"]))
+        validation_csv_path = Path(dl_manager.download_and_extract(_URLS["validation"]))
+        test_csv_path = Path(dl_manager.download_and_extract(_URLS["test"]))
+
+        data_dir = {
+            "train": train_csv_path,
+            "validation": validation_csv_path,
+            "test": test_csv_path,
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["validation"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        df = pd.read_csv(filepath, sep=",", header="infer").reset_index()
+        if self.config.schema == "source":
+            for row in df.itertuples():
+                entry = {
+                    "index": row.index,
+                    "review": row.review,
+                    "ac": row.ac,
+                    "air_panas": row.air_panas,
+                    "bau": row.bau,
+                    "general": row.general,
+                    "kebersihan": row.kebersihan,
+                    "linen": row.linen,
+                    "service": row.service,
+                    "sunrise_meal": row.sunrise_meal,
+                    "tv": row.tv,
+                    "wifi": row.wifi,
+                }
+                yield row.index, entry
+
+        elif self.config.schema == "nusantara_text_multi":
+            for row in df.itertuples():
+                entry = {
+                    "id": str(row.index),
+                    "text": row.review,
+                    "labels": [label for label in row[3:]],
+                }
+                yield row.index, entry