metadata

annotations_creators:
  - expert-generated
language_creators:
  - other
language:
  - pl
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
pretty_name: Polemo2
size_categories:
  - 8K
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification

Polemo2

Description

The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in the 2+1 scheme, which gives a total of 197,046 annotations. About 85% of the reviews are from the medicine and hotel domains. Each review is annotated with four labels: positive, negative, neutral, or ambiguous.

Tasks (input, output and metrics)

The task is to predict the correct label of the review.

Input ('text' column): sentence

Output ('target' column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous)

Domain: Online reviews

Measurements: Accuracy, F1 Macro

Example: Na samym wejściu hotel śmierdzi . W pokojach jest pleśń na ścianach , brudny dywan . W łazience śmierdzi chemią , hotel nie grzeje w pokojach panuje chłód . Wyposażenie pokoju jest stare , kran się rusza , drzwi na balkon nie domykają się . Jedzenie jest w małych ilościach i nie smaczne . Nie polecam nikomu tego hotelu . → 1 (negative)

Data splits

Subset	Cardinality
train	6573
val	823
test	820

Class distribution

Class	train	dev	test
minus	0.3756	0.3694	0.4134
plus	0.2775	0.2868	0.2768
amb	0.1991	0.1883	0.1659
zero	0.1477	0.1555	0.1439

Citation

@inproceedings{kocon-etal-2019-multi,
    title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
    author = "Koco{\'n}, Jan  and
      Mi{\l}kowski, Piotr  and
      Za{\'s}ko-Zieli{\'n}ska, Monika",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/K19-1092",
    doi = "10.18653/v1/K19-1092",
    pages = "980--991",
    abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
}

License

Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Links

HuggingFace

Source

Paper

Examples

Loading

from pprint import pprint

from datasets import load_dataset

dataset = load_dataset("clarin-pl/polemo2-official")
pprint(dataset['train'][0])

# {'target': 1,
#  'text': 'Na samym wejściu hotel śmierdzi . W pokojach jest pleśń na ścianach '
#          ', brudny dywan . W łazience śmierdzi chemią , hotel nie grzeje w '
#          'pokojach panuje chłód . Wyposażenie pokoju jest stare , kran się '
#          'rusza , drzwi na balkon nie domykają się . Jedzenie jest w małych '
#          'ilościach i nie smaczne . Nie polecam nikomu tego hotelu .'}

Evaluation

import random
from pprint import pprint

from datasets import load_dataset, load_metric

dataset = load_dataset("clarin-pl/polemo2-official")
references = dataset["test"]["target"]

# generate random predictions
predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]

acc = load_metric("accuracy")
f1 = load_metric("f1")

acc_score = acc.compute(predictions=predictions, references=references)
f1_score = f1.compute(predictions=predictions, references=references, average='macro')

pprint(acc_score)
pprint(f1_score)

# {'accuracy': 0.2475609756097561}
# {'f1': 0.23747048177471738}