Upload id_sent_emo_mobile_apps.py with huggingface_hub

id_sent_emo_mobile_apps.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.
+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 = """
+@article{riccosan2023,
+  author    = {Riccosan and Saputra, Karen Etania},
+  title     = {Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review},
+  journal   = {Data in Brief},
+  volume    = {50},
+  year      = {2023},
+  doi       = {10.1016/j.dib.2023.109576},
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]
+_DATASETNAME = "id_sent_emo_mobile_apps"
+_DESCRIPTION = """
+This dataset contains manually annotated public reviews of mobile applications in Indonesia.
+Each review is given a sentiment label (positive, negative, neutral) and
+an emotion label (anger, sadness, fear, happiness, love, neutral).
+"""
+_HOMEPAGE = "https://github.com/Ricco48/Multilabel-Sentiment-and-Emotion-Dataset-from-Indonesian-" "Mobile-Application-Review/tree/CreateCodeForPaper"
+_LICENSE = Licenses.CC_BY_NC_ND_4_0.value
+_URL = (
+    "https://github.com/Ricco48/Multilabel-Sentiment-and-Emotion-Dataset-from-Indonesian-Mobile-Application-Review/raw/CreateCodeForPaper/"
+    "Multilabel%20Sentiment%20and%20Emotion%20Dataset%20from%20Indonesian%20Mobile%20Application%20Review/Multilabel%20Sentiment%20and%20Emotion"
+    "%20Dataset%20from%20Indonesian%20Mobile%20Application%20Review.csv"
+)
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS, Tasks.EMOTION_CLASSIFICATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class EmoSentIndMobile(datasets.GeneratorBasedBuilder):
+    """Dataset of Indonesian mobile application reviews manually annotated for emotion and sentiment."""
+
+    SUBSETS = ["emotion", "sentiment"]
+    EMOTION_CLASS_LABELS = ["Sad", "Anger", "Fear", "Happy", "Love", "Neutral"]
+    SENTIMENT_CLASS_LABELS = ["Negative", "Positive", "Neutral"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME
+        )
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema for {subset} subset",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "content": datasets.Value("string"),
+                    "sentiment": datasets.Value("string"),
+                    "emotion": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_text":
+            if "emotion" in self.config.subset_id:
+                labels = self.EMOTION_CLASS_LABELS
+            elif "sentiment" in self.config.subset_id:
+                labels = self.SENTIMENT_CLASS_LABELS
+            features = schemas.text_features(label_names=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."""
+        fp = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": fp},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        df = pd.read_csv(filepath, sep="\t", index_col=None)
+        for index, row in df.iterrows():
+            if self.config.schema == "source":
+                example = {
+                    "content": row["content"],
+                    "sentiment": row["Sentiment"].title(),
+                    "emotion": row["Emotion"].title(),
+                }
+            elif self.config.schema == "seacrowd_text":
+                if "emotion" in self.config.subset_id:
+                    label = row["Emotion"]
+                elif "sentiment" in self.config.subset_id:
+                    label = row["Sentiment"]
+                example = {"id": str(index), "text": row["content"], "label": label.title()}
+            yield index, example