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uit_visfd.py

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+# 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.
+
+"""
+UIT-ViSFD is the Vietnamese Smartphone Feedback Dataset.
+It is an aspect-based sentiment analysis dataset.
+It consists of 11,122 human-annotated comments for mobile e-commerce.
+"""
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+  @InProceedings{10.1007/978-3-030-82147-0_53,
+  author="Luc Phan, Luong
+  and Huynh Pham, Phuc
+  and Thi-Thanh Nguyen, Kim
+  and Khai Huynh, Sieu
+  and Thi Nguyen, Tham
+  and Thanh Nguyen, Luan
+  and Van Huynh, Tin
+  and Van Nguyen, Kiet",
+  editor="Qiu, Han
+  and Zhang, Cheng
+  and Fei, Zongming
+  and Qiu, Meikang
+  and Kung, Sun-Yuan",
+  title="SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence",
+  booktitle="Knowledge Science, Engineering and Management ",
+  year="2021",
+  publisher="Springer International Publishing",
+  address="Cham",
+  pages="647--658",
+  isbn="978-3-030-82147-0"
+  }
+"""
+
+_DATASETNAME = "uit_visfd"
+
+_DESCRIPTION = """
+UIT-ViSFD is the Vietnamese Smartphone Feedback Dataset.
+It is an aspect-based sentiment analysis dataset.
+It consists of 11,122 human-annotated comments for mobile e-commerce.
+"""
+
+_HOMEPAGE = "https://github.com/LuongPhan/UIT-ViSFD"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {_DATASETNAME: "https://github.com/LuongPhan/UIT-ViSFD/raw/main/UIT-ViSFD.zip"}
+
+_SUPPORTED_TASKS = [Tasks.ASPECT_BASED_SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class UITViSFDDataset(datasets.GeneratorBasedBuilder):
+    """
+    Crawled textual feedback from customers about smartphones on a large e-commerce website in Vietnam.
+    The label of the dataset is ten aspects and three polarities.
+    Please read the guidelines in the paper for more information.
+    We randomly divide the dataset into three sets:
+        - Train: 7,786.
+        - Dev: 1,112.
+        - Test: 2,224.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_text_multi",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_text_multi",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    _LABELS = [
+        "BATTERY#Positive",
+        "BATTERY#Neutral",
+        "BATTERY#Negative",
+        "GENERAL#Positive",
+        "GENERAL#Neutral",
+        "GENERAL#Negative",
+        "CAMERA#Positive",
+        "CAMERA#Neutral",
+        "CAMERA#Negative",
+        "FEATURES#Positive",
+        "FEATURES#Neutral",
+        "FEATURES#Negative",
+        "PRICE#Positive",
+        "PRICE#Neutral",
+        "PRICE#Negative",
+        "SER&ACC#Positive",
+        "SER&ACC#Neutral",
+        "SER&ACC#Negative",
+        "PERFORMANCE#Positive",
+        "PERFORMANCE#Neutral",
+        "PERFORMANCE#Negative",
+        "SCREEN#Positive",
+        "SCREEN#Neutral",
+        "SCREEN#Negative",
+        "DESIGN#Positive",
+        "DESIGN#Neutral",
+        "DESIGN#Negative",
+        "STORAGE#Positive",
+        "STORAGE#Neutral",
+        "STORAGE#Negative",
+        "OTHERS",
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {"index": datasets.Value("int64"), "comment": datasets.Value("string"), "n_star": datasets.Value("int64"), "date_time": datasets.Value("string"), "label": datasets.Sequence(feature=datasets.ClassLabel(names=self._LABELS))}
+            )
+
+        elif self.config.schema == "seacrowd_text_multi":
+            features = schemas.text_multi_features(self._LABELS)
+
+        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."""
+
+        data_dir = dl_manager.download_and_extract(_URLS[_DATASETNAME])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "Train.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "Test.csv"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "Dev.csv"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        df = pd.read_csv(filepath, index_col=None)
+
+        def transform_label(label_string):
+            label_string = label_string.strip("{}")
+            label_pairs = label_string.split(";")
+            label_array = []
+            for pair in label_pairs:
+                pair = pair.strip("{}")
+                if pair:
+                    label_array.append(pair)
+            return label_array
+
+        df["label"] = df["label"].apply(transform_label)
+
+        for index, row in df.iterrows():
+
+            if self.config.schema == "source":
+                example = row.to_dict()
+
+            elif self.config.schema == "seacrowd_text_multi":
+
+                example = {
+                    "id": str(row["index"]),
+                    "text": str(row["comment"]),
+                    "labels": row["label"],
+                }
+
+            yield index, example