SemEval2014.py

# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""The lingual SemEval2014 Task5 Reviews Corpus"""

import datasets

_CITATION = """\
@article{2014SemEval,
 title={SemEval-2014 Task 4: Aspect Based Sentiment Analysis},
 author={ Pontiki, M. and D Galanis and Pavlopoulos, J. and Papageorgiou, H. and Manandhar, S. },
 journal={Proceedings of International Workshop on Semantic Evaluation at},
 year={2014},
}
"""

_LICENSE = """\
 Please click on the homepage URL for license details.
"""

_DESCRIPTION = """\
A collection of SemEval2014 specifically designed to aid research in Aspect Based Sentiment Analysis.
"""

_CONFIG = [
 
 # restaurants domain
 "restaurants",
 # laptops domain
 "laptops",
]

_VERSION = "0.0.1"

_HOMEPAGE_URL = "https://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools"
_DOWNLOAD_URL = "https://raw.githubusercontent.com/YaxinCui/ABSADataset/main/SemEval2014Task4/{split}/{domain}_{split}.xml"


class SemEval2014Config(datasets.BuilderConfig):
 """BuilderConfig for SemEval2014Config."""

 def __init__(self, _CONFIG, **kwargs):
 super(SemEval2014Config, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
 self.configs = _CONFIG


class SemEval2014(datasets.GeneratorBasedBuilder):
 """The lingual Amazon Reviews Corpus"""

 BUILDER_CONFIGS = [
 SemEval2014Config(
 name="All",
 _CONFIG=_CONFIG,
 description="A collection of SemEval2014 specifically designed to aid research in lingual Aspect Based Sentiment Analysis.",
 )
 ] + [
 SemEval2014Config(
 name=config,
 _CONFIG=[config],
 description=f"{config} of SemEval2014 specifically designed to aid research in Aspect Based Sentiment Analysis",
 )
 for config in _CONFIG
 ]
 
 BUILDER_CONFIG_CLASS = SemEval2014Config
 DEFAULT_CONFIG_NAME = "All"

 def _info(self):
 return datasets.DatasetInfo(
 description=_DESCRIPTION,
 features=datasets.Features(
 {'text': datasets.Value(dtype='string'),
 'aspectTerms': [
 {'from': datasets.Value(dtype='string'),
 'polarity': datasets.Value(dtype='string'),
 'term': datasets.Value(dtype='string'),
 'to': datasets.Value(dtype='string')}
 ],
 'aspectCategories': [
 {'category': datasets.Value(dtype='string'),
 'polarity': datasets.Value(dtype='string')}
 ],
 'domain': datasets.Value(dtype='string'),
 'sentenceId': datasets.Value(dtype='string')
 }
 ),
 supervised_keys=None,
 license=_LICENSE,
 homepage=_HOMEPAGE_URL,
 citation=_CITATION,
 )

 def _split_generators(self, dl_manager): 

 train_urls = [_DOWNLOAD_URL.format(split="train", domain=config) for config in self.config.configs]
 dev_urls = [_DOWNLOAD_URL.format(split="trial", domain=config) for config in self.config.configs]
 test_urls = [_DOWNLOAD_URL.format(split="test", domain=config) for config in self.config.configs]

 train_paths = dl_manager.download_and_extract(train_urls)
 dev_paths = dl_manager.download_and_extract(dev_urls)
 test_paths = dl_manager.download_and_extract(test_urls)

 return [
 datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"file_paths": train_paths, "domain_list": self.config.configs}),
 datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"file_paths": dev_paths, "domain_list": self.config.configs}),
 datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"file_paths": test_paths, "domain_list": self.config.configs}),
 ]

 def _generate_examples(self, file_paths, domain_list):
 row_count = 0
 assert len(file_paths)==len(domain_list)

 for i in range(len(file_paths)):
 file_path, domain = file_paths[i], domain_list[i]
 semEvalDataset = SemEvalXMLDataset(file_path, domain)

 for example in semEvalDataset.SentenceWithOpinions:
 yield row_count, example
 row_count += 1

from xml.dom.minidom import parse

class SemEvalXMLDataset():
 def __init__(self, file_name, domain):
 # 获得SentenceWithOpinions，一个List包含(reviewId, sentenceId, text, Opinions)

 self.SentenceWithOpinions = []
 self.xml_path = file_name

 self.sentenceXmlList = parse(self.xml_path).getElementsByTagName('sentence')

 for sentenceXml in self.sentenceXmlList:
 
 sentenceId = sentenceXml.getAttribute("id")
 if len(sentenceXml.getElementsByTagName("text")[0].childNodes) < 1:
 # skip no reviews part
 continue
 text = sentenceXml.getElementsByTagName("text")[0].childNodes[0].nodeValue

 aspectTermsXLMList = sentenceXml.getElementsByTagName("aspectTerm")
 aspectTerms = []
 for opinionXml in aspectTermsXLMList:
 # some text maybe have no opinion
 term = opinionXml.getAttribute("term")
 polarity = opinionXml.getAttribute("polarity")
 from_ = opinionXml.getAttribute("from")
 to = opinionXml.getAttribute("to")
 aspectTermDict = {
 "term": term,
 "polarity": polarity,
 "from": from_,
 "to": to
 }
 aspectTerms.append(aspectTermDict)

 # 从小到大排序
 aspectTerms.sort(key=lambda x: x["from"])

 aspectCategoriesXmlList = sentenceXml.getElementsByTagName("aspectCategory")
 aspectCategories = []
 for aspectCategoryXml in aspectCategoriesXmlList:
 category = aspectCategoryXml.getAttribute("category")
 polarity = aspectCategoryXml.getAttribute("polarity")
 aspectCategoryDict = {
 "category": category,
 "polarity": polarity
 }
 aspectCategories.append(aspectCategoryDict)

 self.SentenceWithOpinions.append({
 "text": text, 
 "aspectTerms": aspectTerms,
 "aspectCategories": aspectCategories,
 "domain": domain, 
 "sentenceId": sentenceId
 }
 )