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unisent.py

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+# coding=utf-8
+
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{asgari2020unisent,
+title={UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages},
+author={Asgari, Ehsaneddin and Braune, Fabienne and Ringlstetter, Christoph and Mofrad, Mohammad RK},
+booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC-2020)},
+year={2020},
+organization={European Language Resources Association (ELRA)}
+}
+"""
+_DATASETNAME = "unisent"
+_DESCRIPTION = """\
+UniSent is a universal sentiment lexica for 1000+ languages.
+To build UniSent, the authors use a massively parallel Bible
+corpus to project sentiment information from English to other
+languages for sentiment analysis on Twitter data. 173 of 1404
+languages are spoken in Southeast Asia
+"""
+_URLS = "https://raw.githubusercontent.com/ehsanasgari/UniSent/master/unisent_lexica_v1/{}_unisent_lexicon.txt"
+_HOMEPAGE = "https://github.com/ehsanasgari/UniSent"
+_LANGUAGES = [
+ 'aaz',
+ 'abx',
+ 'ace',
+ 'acn',
+ 'agn',
+ 'agt',
+ 'ahk',
+ 'akb',
+ 'alj',
+ 'alp',
+ 'amk',
+ 'aoz',
+ 'atb',
+ 'atd',
+ 'att',
+ 'ban',
+ 'bbc',
+ 'bcl',
+ 'bgr',
+ 'bgs',
+ 'bgz',
+ 'bhp',
+ 'bkd',
+ 'bku',
+ 'blw',
+ 'blz',
+ 'bnj',
+ 'bpr',
+ 'bps',
+ 'bru',
+ 'btd',
+ 'bth',
+ 'bto',
+ 'bts',
+ 'btx',
+ 'bug',
+ 'bvz',
+ 'bzi',
+ 'cbk',
+ 'ceb',
+ 'cfm',
+ 'cgc',
+ 'clu',
+ 'cmo',
+ 'cnh',
+ 'cnw',
+ 'csy',
+ 'ctd',
+ 'czt',
+ 'dgc',
+ 'dtp',
+ 'due',
+ 'duo',
+ 'ebk',
+ 'fil',
+ 'gbi',
+ 'gdg',
+ 'gor',
+ 'heg',
+ 'hil',
+ 'hlt',
+ 'hnj',
+ 'hnn',
+ 'hvn',
+ 'iba',
+ 'ifa',
+ 'ifb',
+ 'ifk',
+ 'ifu',
+ 'ify',
+ 'ilo',
+ 'ind',
+ 'iry',
+ 'isd',
+ 'itv',
+ 'ium',
+ 'ivb',
+ 'ivv',
+ 'jav',
+ 'jra',
+ 'kac',
+ 'khm',
+ 'kix',
+ 'kje',
+ 'kmk',
+ 'kne',
+ 'kqe',
+ 'krj',
+ 'ksc',
+ 'ksw',
+ 'kxm',
+ 'lao',
+ 'lbk',
+ 'lew',
+ 'lex',
+ 'lhi',
+ 'lhu',
+ 'ljp',
+ 'lsi',
+ 'lus',
+ 'mad',
+ 'mak',
+ 'mbb',
+ 'mbd',
+ 'mbf',
+ 'mbi',
+ 'mbs',
+ 'mbt',
+ 'mej',
+ 'mkn',
+ 'mmn',
+ 'mnb',
+ 'mnx',
+ 'mog',
+ 'mqj',
+ 'mqy',
+ 'mrw',
+ 'msb',
+ 'msk',
+ 'msm',
+ 'mta',
+ 'mtg',
+ 'mtj',
+ 'mvp',
+ 'mwq',
+ 'mwv',
+ 'mya',
+ 'nbe',
+ 'nfa',
+ 'nia',
+ 'nij',
+ 'nlc',
+ 'npy',
+ 'obo',
+ 'pag',
+ 'pam',
+ 'plw',
+ 'pmf',
+ 'pne',
+ 'ppk',
+ 'prf',
+ 'prk',
+ 'pse',
+ 'ptu',
+ 'pww',
+ 'sas',
+ 'sbl',
+ 'sda',
+ 'sgb',
+ 'smk',
+ 'sml',
+ 'sun',
+ 'sxn',
+ 'szb',
+ 'tbl',
+ 'tby',
+ 'tcz',
+ 'tdt',
+ 'tgl',
+ 'tha',
+ 'tih',
+ 'tlb',
+ 'twu',
+ 'urk',
+ 'vie',
+ 'war',
+ 'whk',
+ 'wrs',
+ 'xbr',
+ 'yli',
+ 'yva',
+ 'zom',
+ 'zyp']
+
+_LICENSE = Licenses.CC_BY_NC_ND_4_0.value  # cc-by-nc-nd-4.0
+_LOCAL = False
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class UniSentDataset(datasets.GeneratorBasedBuilder):
+    LABELS = ["NEGATIVE", "POSITIVE"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{lang}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=_DESCRIPTION, schema="source",
+            subset_id=f"{_DATASETNAME}_{lang}"
+        )
+        for lang in _LANGUAGES
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{lang}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=_DESCRIPTION,
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}_{lang}"
+        )
+        for lang in _LANGUAGES
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "word": datasets.Value("string"),
+                    "lexicon": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(label_names=self.LABELS)
+        else:
+            raise Exception(f"Unsupported schema: {self.config.schema}")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        lang = self.config.subset_id.split("_")[-1]
+        url = _URLS.format(lang)
+        data_dir = dl_manager.download_and_extract(url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        with open(filepath, "r", encoding="utf-8") as filein:
+            data_instances = [inst.strip("\n").split("\t") for inst in filein.readlines()]
+
+        for di_idx, data_instance in enumerate(data_instances):
+            word, lexicon = data_instance
+            if self.config.schema == "source":
+                yield di_idx, {"word": word, "lexicon": lexicon}
+            elif self.config.schema == "seacrowd_text":
+                yield di_idx, {"id": di_idx, "text": word, "label": self.LABELS[self._clip_label(int(lexicon))]}
+            else:
+                raise Exception(f"Unsupported schema: {self.config.schema}")
+
+    @staticmethod
+    def _clip_label(label: int) -> int:
+        """
+        Original labels are -1, +1.
+        Clip the label to 0 or 1 to get right index.
+        """
+        return 0 if int(label) < 0 else 1