Closes #51 | Add id_sentiment_analysis dataloader

seacrowd/sea_datasets/id_sentiment_analysis/id_sentiment_analysis.py

@@ -0,0 +1,160 @@
+# 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 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 TASK_TO_SCHEMA, Licenses, Tasks
+
+_CITATION = """\
+@misc{ridife2019idsa,
+  author = {Fe, Ridi},
+  title = {Indonesia Sentiment Analysis Dataset},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\\url{https://github.com/ridife/dataset-idsa}}
+}
+"""
+
+_DATASETNAME = "id_sentiment_analysis"
+
+_DESCRIPTION = """\
+This dataset consists of 10806 labeled Indonesian tweets with their corresponding sentiment analysis: positive, negative, and neutral, up to 2019.
+This dataset was developed in Cloud Experience Research Group, Gadjah Mada University.
+There is no further explanation of the dataset. Contributor found this dataset after skimming through "Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy" (Lin CH and Nuha U, 2023).
+"""
+
+_HOMEPAGE = "https://ridi.staff.ugm.ac.id/2019/03/06/indonesia-sentiment-analysis-dataset/"
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://raw.githubusercontent.com/ridife/dataset-idsa/master/Indonesian%20Sentiment%20Twitter%20Dataset%20Labeled.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+_SUPPORTED_SCHEMA_STRINGS = [f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+
+class IdSentimentAnalysis(datasets.GeneratorBasedBuilder):
+    """This dataset consists of 10806 labeled Indonesian tweets with their corresponding sentiment analysis: positive, negative, and neutral, up to 2019."""
+
+    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}",
+        ),
+    ]
+
+    seacrowd_schema_config: List[SEACrowdConfig] = []
+
+    for seacrowd_schema in _SUPPORTED_SCHEMA_STRINGS:
+
+        seacrowd_schema_config.append(
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{seacrowd_schema}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} {seacrowd_schema} schema",
+                schema=f"{seacrowd_schema}",
+                subset_id=f"{_DATASETNAME}",
+            )
+        )
+
+    BUILDER_CONFIGS.extend(seacrowd_schema_config)
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "sentimen": datasets.Value("int32"),
+                    "tweet": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.SENTIMENT_ANALYSIS]).lower()}":
+            features = schemas.text_features(label_names=[1, -1, 0])
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+        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."""
+
+        path = dl_manager.download_and_extract(_URLS[_DATASETNAME])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "path": path,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, path: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        idx = 0
+
+        if self.config.schema == "source":
+            df = pd.read_csv(path, delimiter="\t")
+
+            for _, row in df.iterrows():
+                yield idx, row.to_dict()
+                idx += 1
+
+        elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.SENTIMENT_ANALYSIS]).lower()}":
+            df = pd.read_csv(path, delimiter="\t")
+
+            df["id"] = df.index
+            df.rename(columns={"sentimen": "label"}, inplace=True)
+            df.rename(columns={"Tweet": "text"}, inplace=True)
+
+            for _, row in df.iterrows():
+                yield idx, row.to_dict()
+                idx += 1
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")