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code_mixed_jv_id.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.
+
+"""
+Code-mixed sentiment analysis of Indonesian language and Javanese language
+using Lexicon based approach
+
+Nowadays mixing one language with another language either in spoken or written
+communication has become a common practice for bilingual speakers in daily
+conversation as well as in social media. Lexicon based approach is one of the
+approaches in extracting the sentiment analysis. This study is aimed to compare
+two lexicon models which are SentiNetWord and VADER in extracting the polarity
+of the code-mixed sentences in Indonesian language and Javanese language. 3,963
+tweets were gathered from two accounts that provide code-mixed tweets.
+Pre-processing such as removing duplicates, translating to English, filter
+special characters, transform lower case and filter stop words were conducted
+on the tweets. Positive and negative word score from lexicon model was then
+calculated using simple mathematic formula in order to classify the polarity.
+By comparing with the manual labelling, the result showed that SentiNetWord
+perform better than VADER in negative sentiments. However, both of the lexicon
+model did not perform well in neutral and positive sentiments. On overall
+performance, VADER showed better performance than SentiNetWord. This study
+showed that the reason for the misclassified was that most of Indonesian
+language and Javanese language consist of words that were considered as
+positive in both Lexicon model.
+
+[nusantara_schema_name] = (text, t2t)
+"""
+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 = """\
+@article{Tho_2021,
+  doi = {10.1088/1742-6596/1869/1/012084},
+  url = {https://doi.org/10.1088/1742-6596/1869/1/012084},
+  year = 2021,
+  month = {apr},
+  publisher = {{IOP} Publishing},
+  volume = {1869},
+  number = {1},
+  pages = {012084},
+  author = {C Tho and Y Heryadi and L Lukas and A Wibowo},
+  title = {Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach},
+  journal = {Journal of Physics: Conference Series},
+  abstract = {Nowadays mixing one language with another language either in
+  spoken or written communication has become a common practice for bilingual
+  speakers in daily conversation as well as in social media. Lexicon based
+  approach is one of the approaches in extracting the sentiment analysis. This
+  study is aimed to compare two lexicon models which are SentiNetWord and VADER
+  in extracting the polarity of the code-mixed sentences in Indonesian language
+  and Javanese language. 3,963 tweets were gathered from two accounts that
+  provide code-mixed tweets. Pre-processing such as removing duplicates,
+  translating to English, filter special characters, transform lower case and
+  filter stop words were conducted on the tweets. Positive and negative word
+  score from lexicon model was then calculated using simple mathematic formula
+  in order to classify the polarity. By comparing with the manual labelling,
+  the result showed that SentiNetWord perform better than VADER in negative
+  sentiments. However, both of the lexicon model did not perform well in
+  neutral and positive sentiments. On overall performance, VADER showed better
+  performance than SentiNetWord. This study showed that the reason for the
+  misclassified was that most of Indonesian language and Javanese language
+  consist of words that were considered as positive in both Lexicon model.}
+}
+"""
+
+_DATASETNAME = "code_mixed_jv_id"
+
+_DESCRIPTION = """\
+Sentiment analysis and machine translation data for Javanese and Indonesian.
+"""
+
+_HOMEPAGE = "https://iopscience.iop.org/article/10.1088/1742-6596/1869/1/012084"
+
+_LICENSE = "cc_by_3.0"
+
+_URLS = {
+    _DATASETNAME: "https://docs.google.com/spreadsheets/d/1mq2VyPEDfXl7K6p5TbRPsaefYwkuy7RQ/export?format=csv&gid=356398080",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS, Tasks.MACHINE_TRANSLATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+_LANGUAGES = ['jav', 'ind']
+_LOCAL = False
+
+LANGUAGES_COLUMNS = {
+    "id": ("text_ind", "text_jav"),
+    "jv": ("text_jav", "text_ind"),
+}
+
+
+class CodeMixedSenti(datasets.GeneratorBasedBuilder):
+    """Code-mixed sentiment analysis for Indonesian and Javanese."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="code_mixed_jv_id_source",
+            version=SOURCE_VERSION,
+            description="code_mixed_jv_id source schema for Javanese and Indonesian",
+            schema="source",
+            subset_id="code_mixed_source",
+        ),
+        NusantaraConfig(
+            name="code_mixed_jv_id_jv_nusantara_text",
+            version=NUSANTARA_VERSION,
+            description="code_mixed_jv_id nusantara_text schema for Javanese",
+            schema="nusantara_text",
+            subset_id="code_mixed_jv",
+        ),
+        NusantaraConfig(
+            name="code_mixed_jv_id_id_nusantara_text",
+            version=NUSANTARA_VERSION,
+            description="code_mixed_jv_id nusantara_text schema for Indonesian",
+            schema="nusantara_text",
+            subset_id="code_mixed_id",
+        ),
+        NusantaraConfig(
+            name="code_mixed_jv_id_nusantara_t2t",
+            version=NUSANTARA_VERSION,
+            description="code_mixed_jv_id nusantara_t2t schema for Javanese and Indonesian",
+            schema="nusantara_t2t",
+            subset_id="code_mixed_jv_id",
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "code_mixed_id_jv_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({
+                "text_jav": datasets.Value("string"),
+                "text_ind": datasets.Value("string"),
+                "label": datasets.Value("int32")
+            })
+        elif self.config.schema == "nusantara_text":
+            features = schemas.text_features(["-1", "0", "1"])
+        elif self.config.schema == "nusantara_t2t":
+            features = schemas.text2text_features
+
+        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."""
+        url = _URLS[_DATASETNAME]
+        path = dl_manager.download_and_extract(url)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path, "split": "train"}),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        df = pd.read_csv(filepath,
+                         skiprows=1,
+                         names=["text_jav", "label", "text_ind"])
+        if self.config.schema == "source":
+            i = 0
+            for row in df.itertuples():
+                ex = {"text_jav": row.text_jav, "text_ind": row.text_ind, "label": row.label}
+                yield i, ex
+                i += 1
+        elif self.config.schema == "nusantara_text":
+            prefix_length = len(_DATASETNAME)
+            start = prefix_length + 1
+            end = prefix_length + 1 + 2
+            language = self.config.name[start:end]
+            keep_column, drop_column = LANGUAGES_COLUMNS[language]
+            df = df.drop(columns=[drop_column]).rename(columns={keep_column: "text"})
+            i = 0
+            for row in df.itertuples():
+                ex = {"id": str(i), "text": row.text, "label": str(row.label)}
+                yield i, ex
+                i += 1
+        elif self.config.schema == "nusantara_t2t":
+            i = 0
+            for row in df.itertuples():
+                ex = {"id": str(i), "text_1": row.text_jav, "text_2": row.text_ind, "text_1_name": "jav", "text_2_name": "ind"}
+                yield i, ex
+                i += 1
+
+if __name__ == "__main__":
+    datasets.load_dataset(__file__)