# Copyright 2020 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. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import xml.etree.ElementTree as ET import os import datasets import datasets.features.features from datasets import ClassLabel _CITATION = """\ @inproceedings{pontiki-etal-2015-semeval, title = "{S}em{E}val-2015 Task 12: Aspect Based Sentiment Analysis", author = "Pontiki, Maria and Galanis, Dimitris and Papageorgiou, Haris and Manandhar, Suresh and Androutsopoulos, Ion", booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)", month = jun, year = "2015", address = "Denver, Colorado", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S15-2082", doi = "10.18653/v1/S15-2082", pages = "486--495", } """ _DESCRIPTION = """\ These are the datasets for Aspect Based Sentiment Analysis (ABSA), Task 12 of SemEval-2015. """ _HOMEPAGE = "https://alt.qcri.org/semeval2015/task12/index.php?id=data-and-tools" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "restaurants": {"trial": "absa-2015_restaurants_trial.xml", "train": "ABSA15_RestaurantsTrain/ABSA-15_Restaurants_Train_Final.xml", "test": "ABSA15_Restaurants_Test.xml"}, "laptops": {"trial": "absa-2015_laptops_trial.xml", "train": "ABSA15_LaptopsTrain/ABSA-15_Laptops_Train_Data.xml", "test": "ABSA15_Laptops_Test.xml"}, "hotels": {"test": "ABSA15_Hotels_Test.xml"}, } class SemEval2015Task12(datasets.GeneratorBasedBuilder): """These are the datasets for Aspect Based Sentiment Analysis (ABSA), Task 12 of SemEval-2015.""" VERSION = datasets.Version("1.1.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig(name="restaurants", version=VERSION, description="Restaurant reviews"), datasets.BuilderConfig(name="laptops", version=VERSION, description="Laptop reviews"), datasets.BuilderConfig(name="hotels", version=VERSION, description="Hotel reviews"), ] # DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset categories = { "restaurants": { "entities": ["RESTAURANT", "FOOD", "DRINKS", "AMBIENCE", "SERVICE", "LOCATION"], "attributes": ["GENERAL", "PRICES", "QUALITY", "STYLE_OPTIONS", "MISCELLANEOUS"] }, "laptops": { "entities": ["LAPTOP", "DISPLAY", "KEYBOARD", "MOUSE", "MOTHERBOARD", "CPU", "FANS_COOLING", "PORTS", "MEMORY", "POWER_SUPPLY", "OPTICAL_DRIVES", "BATTERY", "GRAPHICS", "HARD_DISC", "MULTIMEDIA_DEVICES", "HARDWARE", "SOFTWARE", "OS", "WARRANTY", "SHIPPING", "SUPPORT", "COMPANY"], "attributes": ["GENERAL", "PRICE", "QUALITY", "OPERATION_PERFORMANCE", "USABILITY", "DESIGN_FEATURES", "PORTABILITY", "CONNECTIVITY", "MISCELLANEOUS"] }, "hotels": { "entities": ["HOTEL", "ROOMS", "FACILITIES", "ROOMS_AMENITIES", "SERVICE", "LOCATION", "FOOD_DRINKS"], "attributes": ["GENERAL", "PRICES", "COMFORT", "CLEANLINESS", "QUALITY", "DESIGN_FEATURES", "STYLE_OPTIONS", "MISCELLANEOUS"] }, } polarities = ["positive", "negative", "neutral"] if self.config.name == "restaurants": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "reviewId": datasets.Value(dtype="string"), "sentences": [ { "sentenceId": datasets.Value("string"), "text": datasets.Value("string"), "opinions": [ { "target": datasets.Value("string"), "category": { "entity": datasets.Value("string"), "attribute": datasets.Value("string") }, "polarity": datasets.Value("string"), "from": datasets.Value("string"), "to": datasets.Value("string"), } ] } ] } ) elif self.config.name == "laptops": features = datasets.Features( { "reviewId": datasets.Value(dtype="string"), "sentences": [ { "sentenceId": datasets.Value("string"), "text": datasets.Value("string"), "opinions": [ { "category": { "entity": datasets.Value("string"), "attribute": datasets.Value("string") }, "polarity": datasets.Value("string"), } ] } ] } ) elif self.config.name == "hotels": features = datasets.Features( { "reviewId": datasets.Value(dtype="string"), "sentences": [ { "sentenceId": datasets.Value("string"), "text": datasets.Value("string"), "opinions": [ { "target": datasets.Value("string"), "category": { "entity": datasets.Value("string"), "attribute": datasets.Value("string") }, "polarity": datasets.Value("string"), "from": datasets.Value("string"), "to": datasets.Value("string"), } ] } ] } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(urls) if self.config.name in ["restaurants", "laptops"]: return [ datasets.SplitGenerator( name=datasets.Split("trial"), # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['trial'], "split": "trial", }, ), datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['train'], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['test'], "split": "test" }, ), ] elif self.config.name == "hotels": return [ datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['test'], "split": "test" }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `id_` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. tree = ET.parse(filepath) root = tree.getroot() for id_, review in enumerate(root.iter("Review")): reviewId = review.attrib.get("rid") sentences = [] for sentence in review.iter("sentence"): sentence_dict = {} sentence_dict["sentenceId"] = sentence.get("id") sentence_dict["text"] = sentence.find("text").text opinions = [] for opinion in sentence.iter("Opinion"): opinion_dict = opinion.attrib opinion_dict["category"] = dict(zip(["entity", "attribute"], opinion_dict["category"].split("#"))) opinions.append(opinion_dict) sentence_dict["opinions"] = opinions sentences.append(sentence_dict) yield id_, { "reviewId": reviewId, "sentences": sentences }