Closes #445 | Create dataset loader for malaysia-tweets-with-sentiment-labels

seacrowd/sea_datasets/malaysia_tweets/malaysia_tweets.py

@@ -0,0 +1,152 @@
+# 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 = """\
+@InProceedings{10.1007/978-981-16-8515-6_44,
+author="Juan, Sarah Samson
+and Saee, Suhaila
+and Mohamad, Fitri",
+editor="Alfred, Rayner
+and Lim, Yuto",
+title="Social Versus Physical Distancing: Analysis of Public Health Messages at the Start of COVID-19 Outbreak in Malaysia Using Natural Language Processing",
+booktitle="Proceedings of the 8th International Conference on Computational Science and Technology",
+year="2022",
+publisher="Springer Singapore",
+address="Singapore",
+pages="577--589",
+abstract="The study presents an attempt to analyse how social media netizens in Malaysia responded to the calls for ``Social Distancing'' and ``Physical Distancing'' as the newly recommended social norm was introduced to the world
+as a response to the COVID-19 global pandemic. The pandemic drove a sharp increase in social media platforms' use as a public health communication platform since the first wave of the COVID-19 outbreak in Malaysia in April 2020.
+We analysed thousands of tweets posted by Malaysians daily between January 2020 and August 2021 to determine public perceptions and interactions patterns. The analysis focused on positive and negative reactions
+and the interchanges of uses of the recommended terminologies ``social distancing'' and ``physical distancing''. Using linguistic analysis and natural language processing,
+findings dominantly indicate influences from the multilingual and multicultural values held by Malaysian netizens, as they embrace the concept of distancing as a measure of global public health safety.",
+isbn="978-981-16-8515-6"
+}
+"""
+
+_DATASETNAME = "malaysia_tweets"
+_DESCRIPTION = """\
+This tweet data was extracted from tweets in Malaysia based on keywords
+"social distancing" and "physical distancing". We conducted
+sentiment analysis to understand public opinions on health messages
+during the COVID-19 pandemic. Tweets from January 2020 to July 2021
+were extracted using Python module snscrape and sentiments were obtained
+automatically using Polyglot and MALAYA NLP tools due to multilingual data.
+"""
+
+_HOMEPAGE = "https://github.com/sarahjuan/malaysia-tweets-with-sentiment-labels"
+
+_LANGUAGES = ["zlm,", "eng"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.UNKNOWN.value  # example: Licenses.MIT.value, Licenses.CC_BY_NC_SA_4_0.value, Licenses.UNLICENSE.value, Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://raw.githubusercontent.com/sarahjuan/malaysia-tweets-with-sentiment-labels/main/data/cleaned_tweets_sentiments.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+
+class MalaysiaTweetsDataset(datasets.GeneratorBasedBuilder):
+    """This tweet data was extracted from tweets in Malaysia based on keywords
+    "social distancing" and "physical distancing" from January 2020 to July 2021."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "text"
+
+    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_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+    SENTIMENT_LABEL_CLASSES = ["POSITIVE", "NEGATIVE", "NEUTRAL"]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "Tweet": datasets.Value("string"),
+                    "Sentiment": datasets.ClassLabel(names=self.SENTIMENT_LABEL_CLASSES),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(self.SENTIMENT_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) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        df = pd.read_csv(filepath, encoding="utf-8")
+        if self.config.schema == "source":
+            for idx, row in df.iterrows():
+                yield idx, dict(row)
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for idx, row in df.iterrows():
+                yield idx, {"id": idx, "text": row["Tweet"], "label": row["Sentiment"]}

seacrowd/utils/constants.py

@@ -250,7 +250,7 @@ class Licenses(Enum):
     Tasks.ASPECT_BASED_SENTIMENT_ANALYSIS: "TEXT_MULTI",
     Tasks.DOMAIN_KNOWLEDGE_MULTICLASSIFICATION: "TEXT_MULTI",
     Tasks.ABUSIVE_LANGUAGE_PREDICTION: "TEXT",
-    Tasks.COMPLAINT_DETECTION = "TEXT",
+    Tasks.COMPLAINT_DETECTION: "TEXT",
     Tasks.DOMAIN_KNOWLEDGE_CLASSIFICATION: "TEXT",
     Tasks.SENTIMENT_ANALYSIS: "TEXT",
     Tasks.TAX_COURT_VERDICT: "TEXT",