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import os
import xml.etree.ElementTree as ET
import datasets
from datasets import GeneratorBasedBuilder, DatasetInfo, Split, SplitGenerator, Features, Value, Sequence
_BASE_URL = "https://drive.google.com/uc?export=download&id=15YklLKAaNXomOAkEAMmLAIIFhS9o-ywS"
class UzABSA(GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="uzabsa", version=VERSION,
description="UZABSA dataset for sentiment analysis in Uzbek"),
]
def _info(self):
return DatasetInfo(
features=Features({
"sentence_id": Value("string"),
"text": Value("string"),
"aspect_terms": Sequence({
"term": Value("string"),
"polarity": Value("string"),
"from": Value("int32"),
"to": Value("int32"),
}),
"aspect_categories": Sequence({
"category": Value("string"),
"polarity": Value("string"),
}),
})
)
def _split_generators(self, dl_manager):
# Use the dl_manager to download and cache the data
downloaded_file = dl_manager.download_and_extract(_BASE_URL)
return [
SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": downloaded_file}),
]
def _generate_examples(self, filepath):
tree = ET.parse(filepath)
root = tree.getroot()
for sentence in root.findall("sentence"):
sentence_id = sentence.get("ID")
text = sentence.find("text").text
aspect_terms = []
for aspect_term in sentence.findall("./aspectTerms/aspectTerm"):
aspect_terms.append({
"term": aspect_term.get("term"),
"polarity": aspect_term.get("polarity"),
"from": int(aspect_term.get("from")),
"to": int(aspect_term.get("to")),
})
aspect_categories = []
for aspect_category in sentence.findall("./aspectCategories/aspectCategory"):
aspect_categories.append({
"category": aspect_category.get("category"),
"polarity": aspect_category.get("polarity"),
})
yield sentence_id, {
"sentence_id": sentence_id,
"text": text,
"aspect_terms": aspect_terms,
"aspect_categories": aspect_categories,
}
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