Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
Polish
Size:
100K - 1M
License:
File size: 4,944 Bytes
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---
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](https://huggingface.co./datasets/clarin-pl/polemo2-official)
[Source](https://clarin-pl.eu/dspace/handle/11321/710)
[Paper](https://aclanthology.org/K19-1092/)
## Examples
### Loading
```python
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
```python
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}
```
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