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barasa.py

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+from pathlib import Path
+from typing import List
+
+import datasets
+import pandas as pd
+import codecs
+from collections import namedtuple
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import DEFAULT_NUSANTARA_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Tasks
+
+_DATASETNAME = "barasa"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 langauge code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+_CITATION = """\
+@inproceedings{baccianella-etal-2010-sentiwordnet,
+    title = "{S}enti{W}ord{N}et 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining",
+    author = "Baccianella, Stefano  and
+      Esuli, Andrea  and
+      Sebastiani, Fabrizio",
+    booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
+    month = may,
+    year = "2010",
+    address = "Valletta, Malta",
+    publisher = "European Language Resources Association (ELRA)",
+    url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/769_Paper.pdf",
+    abstract = "In this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications. SENTIWORDNET 3.0 is an improved version of SENTIWORDNET 1.0, a lexical resource publicly available for research purposes, now currently licensed to more than 300 research groups and used in a variety of research projects worldwide. Both SENTIWORDNET 1.0 and 3.0 are the result of automatically annotating all WORDNET synsets according to their degrees of positivity, negativity, and neutrality. SENTIWORDNET 1.0 and 3.0 differ (a) in the versions of WORDNET which they annotate (WORDNET 2.0 and 3.0, respectively), (b) in the algorithm used for automatically annotating WORDNET, which now includes (additionally to the previous semi-supervised learning step) a random-walk step for refining the scores. We here discuss SENTIWORDNET 3.0, especially focussing on the improvements concerning aspect (b) that it embodies with respect to version 1.0. We also report the results of evaluating SENTIWORDNET 3.0 against a fragment of WORDNET 3.0 manually annotated for positivity, negativity, and neutrality; these results indicate accuracy improvements of about 20{\%} with respect to SENTIWORDNET 1.0.",
+}
+
+@misc{moeljadi_2016,
+    title={Neocl/Barasa: Indonesian SentiWordNet},
+    url={https://github.com/neocl/barasa},
+    journal={GitHub},
+    author={Moeljadi, David},
+    year={2016}, month={Mar}
+}
+"""
+
+_DESCRIPTION = """\
+The Barasa dataset is an Indonesian SentiWordNet for sentiment analysis.
+For each term, the pair (POS,ID) uniquely identifies a WordNet (3.0) synset and there are PosScore and NegScore to show the positivity and negativity of the term.
+The objectivity score can be calculated as: ObjScore = 1 - (PosScore + NegScore).
+"""
+
+_HOMEPAGE = "https://github.com/neocl/barasa"
+
+_LICENSE = "MIT"
+
+_URLs = {
+    "senti_wordnet": "https://github.com/neocl/barasa/raw/master/data/SentiWordNet_3.0.0_20130122.txt",
+    "tab": "https://github.com/neocl/barasa/raw/55f669ca3e417e7fa8d0ebafb67700b9c9eeff1d/data/wn-msa-all.tab",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = None
+
+class Barasa(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="barasa_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="Barasa source schema",
+            schema="source",
+            subset_id="barasa",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "barasa_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "index": datasets.Value("string"),
+                    "synset": datasets.Value("string"),
+                    "PosScore": datasets.Value("float32"),
+                    "NegScore": datasets.Value("float32"),
+                    "language": datasets.Value("string"),
+                    "goodness": datasets.Value("string"),
+                    "lemma": datasets.Value("string"),
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        sentiWordnet_tsv_path = Path(dl_manager.download_and_extract(_URLs["senti_wordnet"]))
+        tab_path = Path(dl_manager.download_and_extract(_URLs["tab"]))
+        data_files = {
+            "sentiWordnet": sentiWordnet_tsv_path,
+            "tab": tab_path,
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": [data_files["sentiWordnet"], data_files["tab"]]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path):
+        lines = self.gen_barasa(filepath[0], filepath[1])
+
+        if self.config.schema == "source":
+            for i, row in enumerate(lines):
+                synset, language, goodness, lemma, PosScore, NegScore = row.split('\t')[:6]
+                PosScore = float(PosScore)
+                NegScore = float(NegScore)
+                ex = {
+                    "index": i,
+                    "synset": synset,
+                    "PosScore": PosScore,
+                    "NegScore": NegScore,
+                    "language": language,
+                    "goodness": goodness,
+                    "lemma": lemma,
+                }
+                yield i, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+    def gen_barasa(self, SENTI_WORDNET_FILE, BAHASA_WORDNET_FILE):
+        SynsetInfo = namedtuple('SynsetInfo', ['synset', 'pos', 'neg'])
+        LemmaInfo  = namedtuple('LemmaInfo', ['lemma', 'pos', 'neg'])
+
+        SYNSET_SCORE = {}
+        LEMMA_SCORE = {}
+
+        with codecs.open(SENTI_WORDNET_FILE, encoding='utf-8', mode='r') as SentiWN:
+            for line in SentiWN.readlines():
+                if line.startswith('#') or len(line.strip()) == 0: # ignore comments
+                    continue
+                # strip off end-of-line, then split
+                pos, snum, pscore, nscore, lemma, definition = line.strip().split('\t')
+                synset = '%s-%s' % (snum, pos)
+                SYNSET_SCORE[synset] = SynsetInfo(synset, pscore, nscore)
+
+        newlines = []
+        with codecs.open(BAHASA_WORDNET_FILE, encoding='utf-8', mode='r') as BahasaWN:
+            for line in BahasaWN.readlines():
+                synset, lang, goodness, lemma = line.strip().split('\t')
+                if synset in SYNSET_SCORE:
+                    sscore = SYNSET_SCORE[synset]
+                    LEMMA_SCORE[lemma] = LemmaInfo(lemma, sscore.pos, sscore.neg)
+                    newline = ("%s\t" * 6) % (synset, lang, goodness, lemma, sscore.pos, sscore.neg)
+                newlines.append(newline)
+
+        return newlines