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# 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
}
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